Advances in free-energy-based simulations of protein folding and ligand binding

Cholinergic Inhibition and Antioxidant Potential of Gongronema latifolium Benth Leaf in Neurodegeneration: Experimental and In Silico Study

The use of Gongronema latifolium for the management of various forms of neurological disorders has generated a lot of interest in the need to further investigate its neurotherapeutic constituents. This work, therefore, focused on assessing the inhibitory potential of selected bioactive components derived from G. latifolium against key neurotherapeutic targets and oxidant species associated with neurodegeneration using in vitro analysis and biomolecular modelling. G. latifolium methanol extract (GLME), solvent partition, chromatographic fractions (A-F) of GLME and pregnane compounds (Iloneoside and marsectohexol) derived from fraction-B with the highest activity were investigated for in vitro acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and monoamine oxidase (MAO) inhibition in addition to their in vitro antioxidant activities. The interactions of iloneoside, marsectohexol, and reference drugs with human acetylcholinesterase, butyrylcholinesterase, and β-secretase (BACE-1) were further assessed using molecular docking, binding free energy calculations, cluster analysis, and molecular dynamics simulations. The GLME and fractions inhibited the activities of both acetylcholinesterase and butyrylcholinesterase in a dose-dependent manner. Iloneoside and marsectohexol exhibited in vitro concentration-dependent inhibitory activities against acetylcholinesterase (IC50 = 19.28, 184.9 µM, respectively) and butyrylcholinesterase (IC50 = 30.75, 43.4 µM, respectively). These compounds also possess ferric ion-reducing, hydroxyl, and superoxide radical-scavenging activities. Iloneoside had the highest docking scores of -9.8, -9.9 -9.4 Kcal for AChE, BChE, and BACE1, respectively. The stability of the interaction of the bioactive compounds with the catalytic residues of the protein targets was preserved in a 100 ns molecular dynamics simulation. Iloneoside, a rare pregnane glycoside, was identified as a neurotherapeutic constituent of G. latifolium leaf. Further studies are suggested to investigate the neurotherapeutic potential in animal models.

Identification of potential inhibitors of cholinergic and β-secretase enzymes from phytochemicals derived from Gongronema latifolium Benth leaf: an integrated computational analysis

Neurodegenerative disorders (NDDs) are associated with increased activities of the brain acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and β-secretase enzyme (BACE1). Inhibition of these enzymes affords therapeutic option for managing NDDs such as Alzheimer's disease (AD) and Parkinson's disease (PD). Although, Gongronema latifolium Benth (GL) has been widely documented in ethnopharmacological and scientific reports for the management of NDDs, there is paucity of information on its underlying mechanism and neurotherapeutic constituents. Herein, 152 previously reported Gongronema latifolium derived-phytochemicals (GLDP) were screened against hAChE, hBChE and hBACE-1 using molecular docking, molecular dynamics (MD) simulations, free energy of binding calculations and cluster analysis. The result of the computational analysis identified silymarin, alpha-amyrin and teraxeron with the highest binding energies (-12.3, -11.2, -10.5 Kcal/mol) for hAChE, hBChE and hBACE-1 respectively as compared with those of the reference inhibitors (-12.3, -9.8 and - 9.4 for donepezil, propidium and aminoquinoline compound respectively). These best docked phytochemicals were found to be orientated in the hydrophobic gorge where they interacted with the choline-binding pocket in the A-site and P-site of the cholinesterase and subsites S1, S3, S3' and flip (67-75) residues of the pocket of the BACE-1. The best docked phytochemicals complexed with the target proteins were stable in a 100 ns molecular dynamic simulation. The interactions with the catalytic residues were preserved during the simulation as observed from the MMGBSA decomposition and cluster analyses. The presence of these phytocompounds most notably silymarin, which demonstrated dual high binding tendencies to both cholinesterases, were identified as potential neurotherapeutics subject to further investigation.

Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology

Molecular dynamics (MD) simulations have become a valuable tool in structural biology, offering insights into complex biological systems that are difficult to obtain through experimental techniques alone. The lack of available data sets and structures in most published computational work has limited other researchers' use of these models. In recent years, the emergence of online sharing platforms and MD database initiatives favor the deposition of ensembles and structures to accompany publications, favoring reuse of the data sets. However, the lack of uniform metadata collection, formats, and what data are deposited limits the impact and its use by different communities that are not necessarily experts in MD. This Perspective highlights the need for standardization and better resource sharing for processing and interpreting MD simulation results, akin to efforts in other areas of structural biology. As the field moves forward, we will see an increase in popularity and benefits of MD-based integrative approaches combining experimental data and simulations through probabilistic reasoning, but these too are limited by uniformity in experimental data availability and choices on how the data are modeled that are not trivial to decipher from papers. Other fields have addressed similar challenges comprehensively by establishing task forces with different degrees of success. The large scope and number of communities to represent the breadth of types of MD simulations complicates a parallel approach that would fit all. Thus, each group typically decides what data and which format to upload on servers like Zenodo. Uploading data with FAIR (findable, accessible, interoperable, reusable) principles in mind including optimal metadata collection will make the data more accessible and actionable by the community. Such a wealth of simulation data will foster method development and infrastructure advancements, thus propelling the field forward.

Energy-Scaled Debye-Hückel Theory for the Electrostatic Solvation Free Energy in Size-Asymmetric Electrolyte Solutions

In this report, an energy-scaled Debye-Hückel theory is developed for fast and accurate evaluation of the electrostatic solvation free energy in size-asymmetric electrolyte solutions. A size-asymmetric electrolyte solution is mapped to a dielectric continuum medium with Debye-Hückel-like response. Based on the scaling relation of the electrostatic energy of a spherical ion in the small and large size limits, a Padé polynomial is used to interpolate the electrostatic energy at finite size. The Padé polynomial is further interpreted as the electrostatic energy of an effective Debye-Hückel mean field model, depicted by a modified Debye parameter and a surface charge density due to the size asymmetry of the solvent ions. This theory can distinguish the electrostatic energies and the electrostatic solvation free energies of solutes with the same size but opposite charges. Application to charged hard and charged soft spheres demonstrates the accuracy of our approach.

Applications of Molecular Dynamics Simulations in Drug Discovery

In the current drug development process, molecular dynamics (MD) simulations have proven to be very useful. This chapter provides an overview of the current applications of MD simulations in drug discovery, from detecting protein druggable sites and validating drug docking outcomes to exploring protein conformations and investigating the influence of mutations on its structure and functions. In addition, this chapter emphasizes various strategies to improve the conformational sampling efficiency in molecular dynamics simulations. With a growing computer power and developments in the production of force fields and MD techniques, the importance of MD simulations in helping the drug development process is projected to rise significantly in the future.

Elemental evaluation, nutritional analysis, GC-MS analysis and ameliorative effects of Artocarpus communis J.R.Forst. & G.Forst. seeds' phytoconstituents on metabolic syndrome via in silico approach

The nutritional as well as beneficial effects of the Artocarpus communis seed on metabolic syndrome complications have not been studied. In this research, the aim was to investigate the nutritional composition and beneficial effects of Artocarpus communis seeds' phytoconstituents on the p53 core, fat mass and obesity-associated (FTO) protein and cytochrome P450 CYP11A1 domains. The elements and phytochemicals in the seed were determined through atomic absorption spectroscopy assay and gas chromatography-mass spectrometry (GC-MS) analysis, respectively. Also, the compounds detected were docked to the p53 core, FTO protein and cytochrome P450 CYP11A1 domains protein. Artocarpus communis seed contains sodium (7.824 ± 0.0134 ppm), magnesium (10.187 ± 0.0239 ppm) and iron (1.924 ± 0.0017), while zinc and cadmium were undetected. Phenolics and flavonoids were the most abundant phytochemicals in the seed. Phytoconstituents, such as pentadecanoic acid, hexadecanoic acid and methyl ester, possessing different therapeutic effects were identified via GC-MS analysis. In A. communis seed, 3-methyl-4-nitro-5-(1-pyrazolyl) pyrazole and phenanthrene were able to bind more peculiarly and specifically to the p53 core, FTO protein and cytochrome P450 CYP11A1 domains. One of the important processes that were hypothesized for the recovery of metabolic syndrome in affected victims is shown by the molecular dynamics analysis, which shows that the binding of these chemicals to the targeted structure stabilized the proteins. Therefore, Artocarpus communis seeds could be a new strategy for the management of metabolic syndrome.Communicated by Ramaswamy H. Sarma.

Antispasmodic Effect of Alstonia boonei De Wild. and Its Constituents: Ex Vivo and In Silico Approaches

BACKGROUND

Alstonia boonei, belonging to the family Apocynaceae, is one of the best-known medicinal plants in Africa and Asia. Stem back preparations are traditionally used as muscle relaxants. This study investigated the antispasmodic properties of Alstonia boonei Stem back and its constituents.

METHOD

The freeze-dried aqueous Stem back extract of A. boonei, as well as dichloromethane (DCM), ethyl acetate, and aqueous fractions, were evaluated for their antispasmodic effect via the ex vivo method. Two compounds were isolated from the DCM fraction using chromatographic techniques, and their antispasmodic activity was evaluated. An in silico study was conducted by evaluating the interaction of isolated compounds with human PPARgamma-LBD and human carbonic anhydrase isozyme.

RESULTS

The Stem back crude extract, DCM, ethyl acetate, and aqueous fractions showed antispasmodic activity on high-potassium-induced (K+ 80 mM) contractions on isolated rat ileum with IC50 values of 0.03 ± 0.20, 0.02 ± 0.05, 0.03 ± 0.14, and 0.90 ± 0.06 mg/mL, respectively. The isolated compounds from the DCM fraction were β-amyrin and boonein, with only boonein exhibiting antispasmodic activity on both high-potassium-induced (IC50 = 0.09 ± 0.01 µg/mL) and spontaneous (0.29 ± 0.05 µg/mL) contractions. However, β-amyrin had a stronger interaction with the two proteins during the simulation.

CONCLUSION

The isolated compounds boonein and β-amyrin could serve as starting materials for the development of antispasmodic drugs.

Atomistic simulations suggest dietary flavonoids from Beta vulgaris (beet) as promising inhibitors of human angiotensin-converting enzyme and 2-alpha-adrenergic receptors in hypertension

Motivation

Beta vulgaris (beet) is extensively reported for its antihypertensive activity. However, the mechanismunderpinning its antihypertensive activity is not well understood. In this study, we evaluated the in silico interactionsof 70 compounds derived from beta vulgaris against the active sites of angiotensin-converting enzyme (ACE) and alpha-adrenergic receptor (AR).

Results

Structure-based virtual screening against angiotensin-converting enzyme revealed that, Cochliophilin A (-9.0 Kcal/mol), Miraxanthin (-8.3 Kcal/mol), and quercimeritrin (-9.7 Kcal/mol) had lower docking scores than the reference lisinopril (-7.9 Kcal/mol). These compounds exhibited dual binding tendency as they also ranked top compounds upon screening against adrenergic receptor. The thermodynamic parameters computed from the resulting trajectories obtained from the 100 ns full atomistic molecular dynamics simulation revealed structural stability and conformational flexibility of the ligand-receptor complexes as indicated by the RMSD, RMSF, RoG, SASA, and H-bond calculations. The molecular mechanics with generalized Born and surface area solvation binding energy calculations revealed that the proteins exhibit considerable binding energy with the phytochemicals in a dynamic environment. Furthermore, the hit compounds possess good physicochemical properties and drug-likeness. Overall, cochliophilin and quercimeritrin are promising dual-target directed flavonoids from Beta vulgaris; and are suggested for further experimental and preclinical evaluation.

Availability and implementation

All data was provided in the manuscript.

Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2): Improved Calculations of Ligand Binding Thermodynamics and Kinetics with Closed Protein Pocket

Ligand binding thermodynamics and kinetics are critical parameters for drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics from molecular simulations due to limited simulation timescales. Protein dynamics, especially in the ligand binding pocket, often plays an important role in ligand binding. Based on our previously developed Ligand Gaussian accelerated molecular dynamics (LiGaMD), here we present LiGaMD2 in which a selective boost potential was applied to both the ligand and protein residues in the binding pocket to improve sampling of ligand binding and dissociation. To validate the performance of LiGaMD2, the T4 lysozyme (T4L) mutants with open and closed pockets bound by different ligands were chosen as model systems. LiGaMD2 could efficiently capture repetitive ligand dissociation and binding within microsecond simulations of all T4L systems. The obtained ligand binding kinetic rates and free energies agreed well with available experimental values and previous modeling results. Therefore, LiGaMD2 provides an improved approach to sample opening of closed protein pockets for ligand dissociation and binding, thereby allowing for efficient calculations of ligand binding thermodynamics and kinetics.

Pre-Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding

The latest biological findings observe that the motionless "lock-and-key" theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory-related studies, thus hindering the possibility of supervised learning. A spatial-temporal pre-training method based on the modified equivariant graph matching networks, dubbed ProtMD which has two specially designed self-supervised learning tasks: atom-level prompt-based denoising generative task and conformation-level snapshot ordering task to seize the flexibility information inside molecular dynamics (MD) trajectories with very fine temporal resolutions is presented. The ProtMD can grant the encoder network the capacity to capture the time-dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen to verify the effectiveness of ProtMD through linear detection and task-specific fine-tuning. A huge improvement from current state-of-the-art methods, with a decrease of 4.3% in root mean square error for the binding affinity problem and an average increase of 13.8% in the area under receiver operating characteristic curve and the area under the precision-recall curve for the ligand efficacy problem is observed. The results demonstrate a strong correlation between the magnitude of conformation's motion in the 3D space and the strength with which the ligand binds with its receptor.

African derived phytocompounds may interfere with SARS-CoV-2 RNA capping machinery via inhibition of 2'-O-ribose methyltransferase: An in silico perspective

Despite the ongoing vaccination against the life-threatening COVID-19, there is need for viable therapeutic interventions. The S-adenosyl-l-Methionine (SAM) dependent 2-O'-ribose methyltransferase (2'-O-MTase) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents a therapeutic target against COVID-19 infection. In a bid to profile bioactive principles from natural sources, a custom-made library of 226 phytochemicals from African medicinal plants with especially anti-malarial activity was screened for direct interactions with SARS-CoV-2 2'-O-MTase (S2RMT) using molecular docking and molecular dynamics (MD) simulations as well as binding free energies methods. Based on minimal binding energy lower than sinefungin (a reference methyl-transferase inhibitor) and binding mode analysis at the catalytic site of S2RMT, a list of 26 hit phytocompounds was defined. The interaction of these phytocompounds was compared with the 2'-O-MTase of SARS-CoV and MERS-CoV. Among these compounds, the lead phytocompounds (LPs) viz: mulberrofuran F, 24-methylene cycloartenol, ferulate, 3-benzoylhosloppone and 10-hydroxyusambarensine interacted strongly with the conserved KDKE tetrad within the substrate binding pocket of the 2'-O-MTase of the coronavirus strains which is critical for substrate binding. The thermodynamic parameters analyzed from the MD simulation trajectories of the LPs-S2RMT complexes presented an eminent structural stability and compactness. These LPs demonstrated favorable druggability and in silico ADMET properties over a diverse array of molecular computing descriptors. The LPs show promising prospects in the disruption of S2RMT capping machinery in silico. However, these LPs should be validated via in vitro and in vivo experimental models.

Accelerating Protein Folding Molecular Dynamics Using Inter-Residue Distances from Machine Learning Servers

Recently, predicting the native structures of proteins has become possible using computational molecular physics (CMP)─physics-based force fields sampled with proper statistics─but only for small proteins. Algorithms with better scaling are needed. We describe ML x MELD x MD, a molecular dynamics (MD) method that inputs residue contacts derived from machine learning (ML) servers into MELD, a Bayesian accelerator that preserves detailed-balance statistics. Contacts are derived from trRosetta-predicted distance histograms (distograms) and are integrated into MELD's atomistic MD as spatial restraints through parametrized potential functions. In the CASP14 blind prediction event, ML x MELD x MD predicted 13 native structures to better than 4.5 Å error, including for 10 proteins in the range of 115-250 amino acids long. Also, the scaling of simulation time vs protein length is much better than unguided MD: tsim ∼ e0.023N for ML x MELD x MD vs tsim ∼ e0.168N for MD alone. This shows how machine learning information can be leveraged to advance physics-based modeling of proteins.

Molecular Docking Simulation Studies Identifies Potential Natural Product Derived-Antiwolbachial Compounds as Filaricides against Onchocerciasis

Onchocerciasis is the leading cause of blindness and severe skin lesions which remain a major public health problem, especially in tropical areas. The widespread use of antibiotics and the long duration required for effective treatment continues to add to the increasing global menace of multi-resistant pathogens. Onchocerca volvulus harbors the endosymbiont bacteria Wolbachia, essential for the normal development of embryos, larvae and long-term survival of the adult worm, O. volvulus. We report here results of using structure-based drug design (SBDD) approach aimed at identifying potential novel Wolbachia inhibitors from natural products against the Wolbachia surface protein (WSP). The protein sequence of the WSP with UniProtKB identifier Q0RAI4 was used to model the three-dimensional (3D) structure via homology modelling techniques using three different structure-building algorithms implemented in Modeller, I-TASSER and Robetta. Out of the 15 generated models of WSP, one was selected as the most reasonable quality model which had 82, 15.5, 1.9 and 0.5% of the amino acid residues in the most favored regions, additionally allowed regions, generously allowed regions and disallowed regions, respectively, based on the Ramachandran plot. High throughput virtual screening was performed via Autodock Vina with a library comprising 42,883 natural products from African and Chinese databases, including 23 identified anti-Onchocerca inhibitors. The top six compounds comprising ZINC000095913861, ZINC000095486235, ZINC000035941652, NANPDB4566, acetylaleuritolic acid and rhemannic acid had binding energies of −12.7, −11.1, −11.0, −11, −10.3 and −9.5 kcal/mol, respectively. Molecular dynamics simulations including molecular mechanics Poisson-Boltzmann (MMPBSA) calculations reinforced the stability of the ligand-WSP complexes and plausible binding mechanisms. The residues Arg45, Tyr135, Tyr148 and Phe195 were predicted as potential novel critical residues required for ligand binding in pocket 1. Acetylaleuritolic acid and rhemannic acid (lantedene A) have previously been shown to possess anti-onchocercal activity. This warrants the need to evaluate the anti-WSP activity of the identified molecules. The study suggests the exploitation of compounds which target both pockets 1 and 2, by investigating their potential for effective depletion of Wolbachia. These compounds were predicted to possess reasonably good pharmacological profiles with insignificant toxicity and as drug-like. The compounds were computed to possess biological activity including antibacterial, antiparasitic, anthelmintic and anti-rickettsials. The six natural products are potential novel antiwolbachial agents with insignificant toxicities which can be explored further as filaricides for onchocerciasis.

Global Optimization of the Lennard-Jones Parameters for the Drude Polarizable Force Field

Molecular dynamics (MD) simulations based on atomic models play an important role in the drug-discovery process to screen molecules, estimate binding free energies, and optimize lead compounds in chemical space. Accurate computations of thermodynamic and kinetic properties using MD simulations are highly dependent on the accuracy of the underlying atomic force field. In this context, going beyond the nonpolarizable fixed-charge model by accounting explicitly for induced polarization is highly desirable. The CHARMM polarizable force field based on classical Drude oscillators, in which an auxiliary charged particle is attached via a harmonic spring to its parent nucleus, offers both a computationally convenient and rigorous framework to model explicitly induced electronic polarization in MD simulations. For any molecule of interest, electrostatic partial charges, atomic polarizabilities, and Thole shielding factors, as well as bonded parameters can either be determined from ab initio calculations or ascribed from the knowledge-based library of the CHARMM Generalized force field. While this approach is fairly reliable in general, it is well understood that the overall accuracy of the models with respect to thermodynamic properties such as bulk density, enthalpies, and solvation free energies is particularly sensitive to the nonbonded Lennard-Jones (LJ) parameters. In the present study, we systematically refined the set of LJ parameters for the atom types available in the Drude force field to best match the experimental thermodynamic properties for 416 small druglike organic molecules. To further test the transferability of the optimized parameters, the hydration free energy of 372 molecules was computed. The calculations resulted in a small average error of 0.46 kcal/mol and a Pearson R of 0.9, representing a significant improvement over the additive GAFF force field in our previous study, where an average error of ∼2 kcal/mol was obtained. Such an improvement is consistent with the ability of the polarizable Drude model to more accurately model interactions in different environments. The effort provides a roadmap for the global optimization of force field parameters using experimental data. It is hoped that the present effort will further the application of the Drude polarizable force field in molecular simulations including drug design and discovery.

Dietary stigmastane-type saponins as promising dual-target directed inhibitors of SARS-CoV-2 proteases: a structure-based screening

Despite the development of COVID-19 vaccines, at present, there is still no approved antiviral drug against the pandemic. The SARS-CoV-2 3-chymotrypsin-like proteases (S-3CLpro) and papain-like protease (S-PLpro) are essential for the viral proliferation cycle, hence attractive drug targets. Plant-based dietary components that have been extensively reported for antiviral activities may serve as cheap sources of preventive nutraceuticals and/or antiviral drugs. A custom-made library of 176 phytochemicals from five West African antiviral culinary herbs was screened for potential dual-target-directed inhibitors of S-3CLpro and S-PLpro in silico. The docking analysis revealed fifteen steroidal saponins (FSS) from Vernonia amygdalina with the highest binding tendency for the active sites of S-3CLpro and S-PLpro. In an optimized docking analysis, the FSS were further docked against four equilibrated conformers of the S-3CLpro and S-PLpro. Three stigmastane-type steroidal saponins (vernonioside A2, vernonioside A4 and vernonioside D2) were revealed as the lead compounds. These compounds interacted with the catalytic residues of both S-3CLpro and S-PLpro, thereby exhibiting dual inhibitory potential against these SARS-CoV-2 cysteine proteases. The binding free energy calculations further corroborated the static and optimized docking analysis. The complexed proteases with these promising phytochemicals were stable during a full atomistic MD simulation while the phytochemicals exhibited favourable physicochemical and ADMET properties, hence, recommended as promising inhibitors of SARS-CoV-2 cysteine proteases.

Amyloid β fibrils disruption by kolaviron: Molecular docking and extended molecular dynamics simulation studies

Garcinia kola (GK) produces notable effects against neurodegenerative conditions, including experimentally-induced Alzheimer's disease (AD). These remarkable effects are basically attributable to kolaviron (KV), a bioflavonoid constituent of this seed. Specifically, it has been reported that in AD models, KV produces interesting neuroprotective effects, being able to diminish associated neurotoxicity, via modulation of antioxidative, inflammatory and other disease modifying processes. Intriguingly, the effect of KV on amyloid-beta (Aβ) aggregation and disruption of preformed Aβ fibrils have not been studied. In this study, we have described a thorough computational study on the mechanism of action of KV as an Aβ fibrils disruptor at molecular level. We used comprehensive in silico docking evaluations and extended molecular dynamics simulation to mimic KV/Aβ fibrils system. Results indicate that KV was able to move within the Aβ fibrils, binding with important residues and components in the Aβ peptide identified to be vital for stabilizing preformed fibrils. KV destabilized the assembled Aβ fibrils, indicating the ability KV as a potential anti-amyloidogenic agent. Furthermore, this work highlighted the possibility of identifying new multifunctional phytocompounds as potent AD drugs.

SARS-CoV-2 host cell entry: an in silico investigation of potential inhibitory roles of terpenoids

BACKGROUND

Targeting viral cell entry proteins is an emerging therapeutic strategy for inhibiting the first stage of SARS-CoV-2 infection. In this study, 106 bioactive terpenoids from African medicinal plants were screened through molecular docking analysis against human angiotensin-converting enzyme 2 (hACE2), human transmembrane protease serine 2 (TMPRSS2), and the spike (S) proteins of SARS-CoV-2, SARS-CoV, and MERS-CoV. In silico absorption-distribution-metabolism-excretion-toxicity (ADMET) and drug-likeness prediction, molecular dynamics (MD) simulation, binding free energy calculations, and clustering analysis of MD simulation trajectories were performed on the top docked terpenoids to respective protein targets.

RESULTS

The results revealed eight terpenoids with high binding tendencies to the catalytic residues of different targets. Two pentacyclic terpenoids (24-methylene cycloartenol and isoiguesteri) interacted with the hACE2 binding hotspots for the SARS-CoV-2 spike protein, while the abietane diterpenes were found accommodated within the S1-specificity pocket, interacting strongly with the active site residues TMPRSS2. 3-benzoylhosloppone and cucurbitacin interacted with the RBD and S2 subunit of SARS-CoV-2 spike protein respectively. These interactions were preserved in a simulated dynamic environment, thereby, demonstrating high structural stability. The MM-GBSA binding free energy calculations corroborated the docking interactions. The top docked terpenoids showed favorable drug-likeness and ADMET properties over a wide range of molecular descriptors.

CONCLUSION

The identified terpenoids from this study provides core structure that can be exploited for further lead optimization to design drugs against SARS-CoV-2 cell-mediated entry proteins. They are therefore recommended for further in vitro and in vivo studies towards developing entry inhibitors against the ongoing COVID-19 pandemic.

The binding and specificity of chemokine binding proteins, through the lens of experiment and computation

Chemokines are small proteins that are critical for immune function, being primarily responsible for the activation and chemotaxis of leukocytes. As such, many viruses, as well as parasitic arthropods, have evolved systems to counteract chemokine function in order to maintain virulence, such as binding chemokines, mimicking chemokines, or producing analogs of transmembrane chemokine receptors that strongly bind their targets. The focus of this review is the large group of chemokine binding proteins (CBP) with an emphasis on those produced by mammalian viruses. Because many chemokines mediate inflammation, these CBP could possibly be used pharmaceutically as anti-inflammatory agents. In this review, we summarize the structural properties of a diverse set of CBP and describe in detail the chemokine binding properties of the poxvirus-encoded CBP called vCCI (viral CC Chemokine Inhibitor). Finally, we describe the current and emerging capabilities of combining computational simulation, structural analysis, and biochemical/biophysical experimentation to understand, and possibly re-engineer, protein–protein interactions.

Dual targeting of cytokine storm and viral replication in COVID-19 by plant-derived steroidal pregnanes: An in silico perspective

The high morbidity and mortality rate of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection arises majorly from the Acute Respiratory Distress Syndrome and "cytokine storm" syndrome, which is sustained by an aberrant systemic inflammatory response and elevated pro-inflammatory cytokines. Thus, phytocompounds with broad-spectrum anti-inflammatory activity that target multiple SARS-CoV-2 proteins will enhance the development of effective drugs against the disease. In this study, an in-house library of 117 steroidal plant-derived pregnanes (PDPs) was docked in the active regions of human glucocorticoid receptors (hGRs) in a comparative molecular docking analysis. Based on the minimal binding energy and a comparative dexamethasone binding mode analysis, a list of top twenty ranked PDPs docked in the agonist conformation of hGR, with binding energies ranging between -9.8 and -11.2 kcal/mol, was obtained and analyzed for possible interactions with the human Janus kinases 1 and Interleukins-6 and SARS-CoV-2 3-chymotrypsin-like protease, Papain-like protease and RNA-dependent RNA polymerase. For each target protein, the top three ranked PDPs were selected. Eight PDPs (bregenin, hirundigenin, anhydroholantogenin, atratogenin A, atratogenin B, glaucogenin A, glaucogenin C and glaucogenin D) with high binding tendencies to the catalytic residues of multiple targets were identified. A high degree of structural stability was observed from the 100 ns molecular dynamics simulation analyses of glaucogenin C and hirundigenin complexes of hGR. The selected top-eight ranked PDPs demonstrated high druggable potentials and favourable in silico ADMET properties. Thus, the therapeutic potentials of glaucogenin C and hirundigenin can be explored for further in vitro and in vivo studies.

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

The Role of Configurational Entropy in Miniprotein Stability

Predicting protein stability is a challenge due to the many competing thermodynamic effects. Through de novo protein design, one begins with a target structure and searches for a sequence that will fold into it. Previous work by Rocklin et al. introduced a data set of more than 16,000 miniproteins spanning four structural topologies with information on stability. These structures were characterized with a set of 46 structural descriptors, with no explicit inclusion of configurational entropy (S_cnf). Our work focused on creating a set of 17 descriptors intended to capture variations in S_cnf and its comparison to an extended set of 113 structural and energy model features that extend the Rocklin et al. feature set (R). The S_cnf descriptors statistically discriminate between stable and unstable distributions within topologies and best describe EEHEE topology stability (where E = β sheet and H = α helix). Between 50 and 80% of the variation in each S_cnf descriptor is described by linear combinations of R features. Despite containing useful information about minipeptide stability, providing S_cnf features as inputs to machine learning models does not improve overall performance when predicting protein stability, as the R features sufficiently capture the implicit variations.

A Molecular Modeling Approach to Identify Potential Antileishmanial Compounds Against the Cell Division Cycle (cdc)-2-Related Kinase 12 (CRK12) Receptor of Leishmania donovani

The huge burden of leishmaniasis caused by the trypanosomatid protozoan parasite Leishmania is well known. This illness was included in the list of neglected tropical diseases targeted for elimination by the World Health Organization. However, the increasing evidence of resistance to existing antimonial drugs has made the eradication of the disease difficult to achieve, thus warranting the search for new drug targets. We report here studies that used computational methods to identify inhibitors of receptors from natural products. The cell division cycle-2-related kinase 12 (CRK12) receptor is a plausible drug target against Leishmania donovani. This study modelled the 3D molecular structure of the L. donovani CRK12 (LdCRK12) and screened for small molecules with potential inhibitory activity from African flora. An integrated library of 7722 African natural product-derived compounds and known inhibitors were screened against the LdCRK12 using AutoDock Vina after performing energy minimization with GROMACS 2018. Four natural products, namely sesamin (NANPDB1649), methyl ellagic acid (NANPDB1406), stylopine (NANPDB2581), and sennecicannabine (NANPDB6446) were found to be potential LdCRK12 inhibitory molecules. The molecular docking studies revealed two compounds NANPDB1406 and NANPDB2581 with binding affinities of -9.5 and -9.2 kcal/mol, respectively, against LdCRK12 which were higher than those of the known inhibitors and drugs, including GSK3186899, amphotericin B, miltefosine, and paromomycin. All the four compounds were predicted to have inhibitory constant (Ki) values ranging from 0.108 to 0.587 μM. NANPDB2581, NANPDB1649 and NANPDB1406 were also predicted as antileishmanial with Pa and Pi values of 0.415 and 0.043, 0.391 and 0.052, and 0.351 and 0.071, respectively. Molecular dynamics simulations coupled with molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) computations reinforced their good binding mechanisms. Most compounds were observed to bind in the ATP binding pocket of the kinase domain. Lys488 was predicted as a key residue critical for ligand binding in the ATP binding pocket of the LdCRK12. The molecules were pharmacologically profiled as druglike with inconsequential toxicity. The identified molecules have scaffolds that could form the backbone for fragment-based drug design of novel leishmanicides but warrant further studies to evaluate their therapeutic potential.

Protein storytelling through physics

Every protein has a story-how it folds, what it binds, its biological actions, and how it misbehaves in aging or disease. Stories are often inferred from a protein's shape (i.e., its structure). But increasingly, stories are told using computational molecular physics (CMP). CMP is rooted in the principled physics of driving forces and reveals granular detail of conformational populations in space and time. Recent advances are accessing longer time scales, larger actions, and blind testing, enabling more of biology's stories to be told in the language of atomistic physics.

Cheminformatics-Based Identification of Potential Novel Anti-SARS-CoV-2 Natural Compounds of African Origin

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome virus 2 (SARS-CoV-2) has impacted negatively on public health and socioeconomic status, globally. Although, there are currently no specific drugs approved, several existing drugs are being repurposed, but their successful outcomes are not guaranteed. Therefore, the search for novel therapeutics remains a priority. We screened for inhibitors of the SARS-CoV-2 main protease and the receptor-binding domain of the spike protein from an integrated library of African natural products, compounds generated from machine learning studies and antiviral drugs using AutoDock Vina. The binding mechanisms between the compounds and the proteins were characterized using LigPlot+ and molecular dynamics simulations techniques. The biological activities of the hit compounds were also predicted using a Bayesian-based approach. Six potential bioactive molecules NANPDB2245, NANPDB2403, fusidic acid, ZINC000095486008, ZINC0000556656943 and ZINC001645993538 were identified, all of which had plausible binding mechanisms with both viral receptors. Molecular dynamics simulations, including molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) computations revealed stable protein-ligand complexes with all the compounds having acceptable free binding energies <-15 kJ/mol with each receptor. NANPDB2245, NANPDB2403 and ZINC000095486008 were predicted as antivirals; ZINC000095486008 as a membrane permeability inhibitor; NANPDB2403 as a cell adhesion inhibitor and RNA-directed RNA polymerase inhibitor; and NANPDB2245 as a membrane integrity antagonist. Therefore, they have the potential to inhibit viral entry and replication. These drug-like molecules were predicted to possess attractive pharmacological profiles with negligible toxicity. Novel critical residues identified for both targets could aid in a better understanding of the binding mechanisms and design of fragment-based de novo inhibitors. The compounds are proposed as worthy of further in vitro assaying and as scaffolds for the development of novel SARS-CoV-2 therapeutic molecules.

Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation

Absolute binding free energy calculations with explicit solvent molecular simulations can provide estimates of protein-ligand affinities, and thus reduce the time and costs needed to find new drug candidates. However, these calculations can be complex to implement and perform. Here, we introduce the software BAT.py, a Python tool that invokes the AMBER simulation package to automate the calculation of binding free energies for a protein with a series of ligands. The software supports the attach-pull-release (APR) and double decoupling (DD) binding free energy methods, as well as the simultaneous decoupling-recoupling (SDR) method, a variant of double decoupling that avoids numerical artifacts associated with charged ligands. We report encouraging initial test applications of this software both to re-rank docked poses and to estimate overall binding free energies. We also show that it is practical to carry out these calculations cheaply by using graphical processing units in common machines that can be built for this purpose. The combination of automation and low cost positions this procedure to be applied in a relatively high-throughput mode and thus stands to enable new applications in early-stage drug discovery.

In silico investigation on alkaloids of Rauwolfia serpentina as potential inhibitors of 3-hydroxy-3-methyl-glutaryl-CoA reductase

Present work aimed to investigate the in silico activity of the alkaloids of roots of Rauwolfia serpentina as inhibitors of 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGCR). For this purpose, the three-dimensional (3D) structure of the protein HMGCR (PDB ID: 1HW9) was downloaded from Protein Data Bank (PDB) database, as a target enzyme. The structures of twelve alkaloids from the roots of R. serpentina were selected as ligands and docked with the selected HMGCR enzyme using Molegro Virtual Docker (MVD) software. The software ‘MVD’ computes the binding (atom) energies of selected protein (enzyme) and each ligand at minimum energetic conformation state by using the PLP (Piecewise Linear Potential) scoring mechanism. Docking results of twelve tested alkaloids showed that five alkaloids including compound 1 (ajmalicine), 2 (reserpine), 3 (indobinine), 4 (yohimbine), and 5 (indobine) have displayed the highest MolDock scores and best fit within the prominent active site residues (positioned between 684 and 692 of cis-loop) of HMGCR. According to the lowest MolDock energies obtained through non-covalent interactions of alkaloids with HMGCR, these are characterized to be the potential inhibitors of HMGCR. Therefore, the alkaloids from R. serpentina can effectively suppress the cholesterol biosynthesis pathway through inhibition of HMGCR and can serve as potential lead compounds for the development of new drugs for the treatment of hyperlipidaemia.

Sampling of the conformational landscape of small proteins with Monte Carlo methods

Computer simulation provides an increasingly realistic picture of large-scale conformational change of proteins, but investigations remain fundamentally constrained by the femtosecond timestep of molecular dynamics simulations. For this reason, many biologically interesting questions cannot be addressed using accessible state-of-the-art computational resources. Here, we report the development of an all-atom Monte Carlo approach that permits the modelling of the large-scale conformational change of proteins using standard off-the-shelf computational hardware and standard all-atom force fields. We demonstrate extensive thermodynamic characterization of the folding process of the α-helical Trp-cage, the Villin headpiece and the β-sheet WW-domain. We fully characterize the free energy landscape, transition states, energy barriers between different states, and the per-residue stability of individual amino acids over a wide temperature range. We demonstrate that a state-of-the-art intramolecular force field can be combined with an implicit solvent model to obtain a high quality of the folded structures and also discuss limitations that still remain.

Binding Modes and Selectivity of Cannabinoid 1 (CB1) and Cannabinoid 2 (CB2) Receptor Ligands

The cannabinoid (CB) receptors (CB₁R and CB₂R) represent a promising therapeutic target for several indications such as nociception and obesity. The ligands with nonselectivity can be traced to the high similarity in the binding sites of both cannabinoid receptors. Therefore, the need for selectivity, potency, and G-protein coupling bias has further complicated the design of desired compounds. The bias of currently studied cannabinoid agonists is seldom investigated, and agonists that do exhibit bias are typically nonselective. However, certain long-chain endocannabinoids represent a class of selective and potent CB₁R agonists. The binding mode for this class of compounds has remained elusive, limiting the implementation of its binding features to currently studied agonists. Hence, in the present study, the binding poses for these long-chain cannabinoids, along with other interesting ligands, with the receptors have been determined, by using a combination of molecular docking and molecular dynamics (MD) simulations along with molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations. The binding poses for the long-chain cannabinoids implicate that a site surrounded by the transmembrane (TM)2, TM7, and extracellular loop (ECL)2 is vital for providing the long-chain ligands with the selectivity for CB₁R, especially I267 of CB₁R (corresponding to L182 of CB₂R). Based on the obtained binding modes, the calculated relative binding free energies and selectivity are all in good agreement with the corresponding experimental data, suggesting that the determined binding poses are reasonable. The computational strategy used in this study may also prove fruitful in applications with other GPCRs or membrane-bound proteins.

How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs

G protein-coupled receptors (GPCRs) are implicated in nearly every physiological process in the human body and therefore represent an important drug targeting class. Advances in X-ray crystallography and cryo-electron microscopy (cryo-EM) have provided multiple static structures of GPCRs in complex with various signaling partners. However, GPCR functionality is largely determined by their flexibility and ability to transition between distinct structural conformations. Due to this dynamic nature, a static snapshot does not fully explain the complexity of GPCR signal transduction. Molecular dynamics (MD) simulations offer the opportunity to simulate the structural motions of biological processes at atomic resolution. Thus, this technique can incorporate the missing information on protein flexibility into experimentally solved structures. Here, we review the contribution of MD simulations to complement static structural data and to improve our understanding of GPCR physiology and pharmacology, as well as the challenges that still need to be overcome to reach the full potential of this technique.

Accuracy of Molecular Simulation-Based Predictions of koff Values: A Metadynamics Study

The k_off values of ligands unbinding to proteins are key parameters for drug discovery. Their predictions based on molecular simulation may under- or overestimate experiment in a system- and/or technique-dependent way. Here we use an established method-infrequent metadynamics, based on the AMBER force field-to compute the k_off of the ligand iperoxo (in clinical use) targeting the muscarinic receptor M₂. The ligand charges are calculated by either (i) the Amber standard procedure or (ii) B3LYP-DFT. The calculations using (i) turn out not to provide a reasonable estimation of the transition-state free energy. Those using (ii) differ from experiment by 2 orders of magnitude. On the basis of B3LYP DFT QM/MM simulations, we suggest that the observed discrepancy in (ii) arises, at least in part, from the lack of electronic polarization and/or charge transfer in biomolecular force fields. These issues might be present in other systems, such as DNA-protein complexes.

Alchemical free energy calculations via metadynamics: Application to the theophylline-RNA aptamer complex

We propose a computational workflow for robust and accurate prediction of both binding poses and their affinities at early stage in designing drug candidates. Small, rigid ligands with few intramolecular degrees of freedom, for example, fragment-like molecules, have multiple binding poses, even at a single binding site, and their affinities are often close to each other. We explore various structures of ligand binding to a target through metadynamics using a small number of collective variables, followed by reweighting to obtain the atomic coordinates. After identifying each binding pose by cluster analysis, we perform alchemical free energy calculations on each structure to obtain the overall value. We applied this protocol in computing free energy of binding for the theophylline-RNA aptamer complex. Of the six (meta)stable structures found, the most favorable binding structure is consistent with the structure obtained by NMR. The overall free energy of binding reproduces the experimental values very well.

Structure-function investigation of 3-methylaspartate ammonia lyase reveals substrate molecular determinants for the deamination reaction

The enzymatic reactions leading to the deamination of β-lysine, lysine, or 2-aminoadipic acid are of great interest for the metabolic conversion of lysine to adipic acid. Enzymes able to carry out these reactions are not known, however ammonia lyases (EC 4.3.1.-) perform deamination on a wide range of substrates. We have studied 3-methylaspartate ammonia lyase (MAL, EC 4.3.1.2) as a potential candidate for protein engineering to enable deamination towards β-lysine, that we have shown to be a competitive inhibitor of MAL. We have characterized MAL activity, binding and inhibition properties on six different compounds that would allow to define the molecular determinants necessary for MAL to deaminate our substrate of interest. Docking calculations showed that β-lysine as well as the other compounds investigated could fit spatially into MAL catalytic pocket, although they probably are weak or very transient binders and we identified molecular determinants involved in the binding of the substrate. The hydrophobic interactions formed by the methyl group of 3-methylaspartic acid, together with the presence of the amino group on carbon 2, play an essential role in the appropriate binding of the substrate. The results showed that β-lysine is able to fit and bind in MAL catalytic pocket and can be potentially converted from inhibitor to substrate of MAL upon enzyme engineering. The characterization of the binding and inhibition properties of the substrates tested here provide the foundation for future and more extensive studies on engineering MAL that could lead to a MAL variant able to catalyse this challenging deamination reaction.

Probing the protein corona around charged macromolecules: interpretation of isothermal titration calorimetry by binding models and computer simulations

Isothermal titration calorimetry (ITC) is a widely used tool to experimentally probe the heat signal of the formation of the protein corona around macromolecules or nanoparticles. If an appropriate binding model is applied to the ITC data, the heat of binding and the binding stoichiometry as well as the binding affinity per protein can be quantified and interpreted. However, the binding of the protein to the macromolecule is governed by complex microscopic interactions. In particular, due to the steric and electrostatic protein–protein interactions within the corona as well as cooperative, charge renormalization effects of the total complex, the application of standard (e.g., Langmuir) binding models is questionable and the development of more appropriate binding models is very challenging. Here, we discuss recent developments in the interpretation of the Langmuir model applied to ITC data of protein corona formation, exemplified for the well-defined case of lysozyme coating highly charged dendritic polyglycerol sulfate (dPGS), and demonstrate that meaningful data can be extracted from the fits if properly analyzed. As we show, this is particular useful for the interpretation of ITC data by molecular computer simulations where binding affinities can be calculated but it is often not clear how to consistently compare them with the ITC data. Moreover, we discuss the connection of Langmuir models to continuum binding models (where no discrete binding sites have to be assumed) and their possible extensions toward the inclusion of leading order cooperative electrostatic effects.

Binding Free Energies of Conformationally Disordered Peptides Through Extensive Sampling and End-Point Methods

The ability to obtain binding free energies from molecular simulation techniques provides a valuable support to the interpretation and design of experiments. Among all methods available, the most widely used equilibrium free energy methods range from highly accurate and computationally expensive perturbation theory-based methods, such as free energy perturbation (FEP), or thermodynamic integration (TI), through end-point methods, such as molecular mechanics with generalized Born and surface area solvation (MM/GBSA) or MM/PBSA, when the Poisson-Boltzmann method is used instead of GB, and linear interaction energy (LIE) methods, to scoring functions, which are relatively simple empirical functions widely used as part of molecular docking protocols. Because the use of FEP and TI approaches is restricted to cases where the perturbation leading from an initial to final state is negligible or minimal, their application to cases where large conformational changes are involved between bound and unbound states is rather complex, if not prohibitive in terms of convergence. Here we describe a protocol that involves the use of extensive conformational sampling through molecular dynamics (MD) in combination with end-point methods (MM/GB(PB)SA) with an additional quasi-harmonic entropy component, for the calculation of the relative binding free energies of highly flexible, or intrinsically disordered, peptides to a structured receptor.

Advances in molecular simulations of protein mechanical properties and function

Single-molecule force spectroscopy and classical molecular dynamics are natural allies. Recent advances in both experiments and simulations have increasingly facilitated a direct comparison of SMFS and MD data, most importantly by closing the gap between time scales, which has been traditionally at least 5 orders of magnitudes wide. In this review, we will explore these advances chiefly on the computational side. We focus on protein dynamics under force and highlight recent studies that showcase how lower loading rates and more statistics help to better interpret previous experiments and to also motivate new ones. At the same time, steadily increasing system sizes are used to mimic more closely the mechanical environment in the biological context. We showcase some of these advances on atomistic and coarse-grained scale, from asymmetric membrane tension to larger (multidomain/multimeric) protein assemblies under force.

Large-scale state-dependent membrane remodeling by a transporter protein

That channels and transporters can influence the membrane morphology is increasingly recognized. Less appreciated is that the extent and free-energy cost of these deformations likely varies among different functional states of a protein, and thus, that they might contribute significantly to defining its mechanism. We consider the trimeric Na⁺-aspartate symporter Glt_Ph, a homolog of an important class of neurotransmitter transporters, whose mechanism entails one of the most drastic structural changes known. Molecular simulations indicate that when the protomers become inward-facing, they cause deep, long-ranged, and yet mutually-independent membrane deformations. Using a novel simulation methodology, we estimate that the free-energy cost of this membrane perturbation is in the order of 6–7 kcal/mol per protomer. Compensating free-energy contributions within the protein or its environment must thus stabilize this inward-facing conformation for the transporter to function. We discuss these striking results in the context of existing experimental observations for this and other transporters.

ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution

Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled φ/ψ parameters using 2D φ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in aqueous solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, and to compare to results using other Amber models, we have performed a total of ∼5 ms MD simulations in explicit solvent. Our results show that after amino-acid-specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino-acid-specific Protein Data Bank (PDB) Ramachandran maps better but also shows significantly improved capability to differentiate amino-acid-dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated for by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. Of the explicit water models tested here, we recommend use of OPC with ff19SB.

Absolute Free Energy of Binding Calculations for Macrophage Migration Inhibitory Factor in Complex with a Druglike Inhibitor

Calculation of the absolute free energy of binding (ΔG_bind) for a complex in solution is challenging owing to the need for adequate configurational sampling and an accurate energetic description, typically with a force field (FF). In this study, Monte Carlo (MC) simulations with improved side-chain and backbone sampling are used to assess ΔG_bind for the complex of a druglike inhibitor (MIF180) with the protein macrophage migration inhibitory factor (MIF) using free energy perturbation (FEP) calculations. For comparison, molecular dynamics (MD) simulations were employed as an alternative sampling method for the same system. With the OPLS-AA/M FF and CM5 atomic charges for the inhibitor, the ΔG_bind results from the MC/FEP and MD/FEP simulations, -8.80 ± 0.74 and -8.46 ± 0.85 kcal/mol, agree well with each other and with the experimental value of -8.98 ± 0.28 kcal/mol. The convergence of the results and analysis of the trajectories indicate that sufficient sampling was achieved for both approaches. Repeating the MD/FEP calculations using current versions of the CHARMM and AMBER FFs led to a 6 kcal/mol range of computed ΔG_bind. These results show that calculation of accurate ΔG_bind for large ligands is both feasible and numerically equivalent, within error limits, using either methodology.

Learning from the ligand: using ligand-based features to improve binding affinity prediction

Motivation

Machine learning scoring functions for protein–ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use features describing interactions derived from the protein–ligand complex, with limited information about the chemical or topological properties of the ligand itself.

Results

We demonstrate that the performance of machine learning scoring functions are consistently improved by the inclusion of diverse ligand-based features. For example, a Random Forest (RF) combining the features of RF-Score v3 with RDKit molecular descriptors achieved Pearson correlation coefficients of up to 0.836, 0.780 and 0.821 on the PDBbind 2007, 2013 and 2016 core sets, respectively, compared to 0.790, 0.746 and 0.814 when using the features of RF-Score v3 alone. Excluding proteins and/or ligands that are similar to those in the test sets from the training set has a significant effect on scoring function performance, but does not remove the predictive power of ligand-based features. Furthermore a RF using only ligand-based features is predictive at a level similar to classical scoring functions and it appears to be predicting the mean binding affinity of a ligand for its protein targets.

Availability and implementation

Data and code to reproduce all the results are freely available at http://opig.stats.ox.ac.uk/resources.

Supplementary information

Supplementary data are available at Bioinformatics online.

Evaluation of template-based modeling in CASP13

Performance in the template‐based modeling (TBM) category of CASP13 is assessed here, using a variety of metrics. Performance of the predictor groups that participated is ranked using the primary ranking score that was developed by the assessors for CASP12. This reveals that the best results are obtained by groups that include contact predictions or inter‐residue distance predictions derived from deep multiple sequence alignments. In cases where there is a good homolog in the wwPDB (TBM‐easy category), the best results are obtained by modifying a template. However, for cases with poorer homologs (TBM‐hard), very good results can be obtained without using an explicit template, by deep learning algorithms trained on the wwPDB. Alternative metrics are introduced, to allow testing of aspects of structural models that are not addressed by traditional CASP metrics. These include comparisons to the main‐chain and side‐chain torsion angles of the target, and the utility of models for solving crystal structures by the molecular replacement method. The alternative metrics are poorly correlated with the traditional metrics, and it is proposed that modeling has reached a sufficient level of maturity that the best models should be expected to satisfy this wider range of criteria.

Accelerated Molecular Dynamics Simulation for Helical Proteins Folding in Explicit Water

In this study, we examined the folding processes of eight helical proteins (2I9M, TC5B, 1WN8, 1V4Z, 1HO2, 1HLL, 2KFE, and 1YYB) at room temperature using the explicit solvent model under the AMBER14SB force field with the accelerated molecular dynamics (AMD) and traditional molecular dynamics (MD), respectively. We analyzed and compared the simulation results obtained by these two methods based on several aspects, such as root mean square deviation (RMSD), native contacts, cluster analysis, folding snapshots, free energy landscape, and the evolution of the radius of gyration, which showed that these eight proteins were successfully and consistently folded into the corresponding native structures by AMD simulations carried out at room temperature. In addition, the folding occurred in the range of 40~180 ns after starting from the linear structures of the eight proteins at 300 K. By contrast, these stable folding structures were not found when the traditional molecular dynamics (MD) simulation was used. At the same time, the influence of high temperatures (350, 400, and 450 K) is also further investigated. Study found that the simulation efficiency of AMD is higher than that of MD simulations, regardless of the temperature. Of these temperatures, 300 K is the most suitable temperature for protein folding for all systems. To further investigate the efficiency of AMD, another trajectory was simulated for eight proteins with the same linear structure but different random seeds at 300 K. Both AMD trajectories reached the correct folded structures. Our result clearly shows that AMD simulation are a highly efficient and reliable method for the study of protein folding.

Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins

Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.

Influence of substrates and inhibitors on the structure of Klebsiella pneumoniae carbapenemase-2

The hydrolysis of last resort carbapenem antibiotics by Klebsiella pneumoniae carbapenemase-2 (KPC-2) presents a significant danger to global health. Combined with horizontal gene transfer, the emergence KPC-2 threatens to quickly expand carbapenemase activity to ever increasing numbers of pathogens. Our understanding of KPC-2 has greatly increased over the past decade thanks, in great part, to 20 crystal structures solved by groups around the world. These include apo KPC-2 structures, along with structures featuring a library of 10 different inhibitors representing diverse structural and functional classes. Herein we focus on cataloging the available KPC-2 structures and presenting a discussion of key aspects of each structure and important relationships between structures. Although the available structures do not provide information on dynamic motions with KPC-2, and the family of structures indicates small conformational changes across a wide array of bound inhibitors, substrates, and products, the structures provide a strong foundation for additional studies in the coming years to discover new KPC-2 inhibitors.

Impact statement

The work herein is important to the field as it provides a clear and succinct accounting of available KPC-2 structures. The work advances the field by collecting and analyzing differences and similarities across the available structures. This work features new analyses and interpretations of the existing structures which will impact the field in a positive way by making structural insights more widely available among the beta-lactamase community.

Structure and Dynamics of the EGF Receptor as Revealed by Experiments and Simulations and Its Relevance to Non-Small Cell Lung Cancer

The epidermal growth factor receptor (EGFR) is historically the prototypical receptor tyrosine kinase, being the first cloned and the first where the importance of ligand-induced dimer activation was ascertained. However, many years of structure determination has shown that EGFR is not completely understood. One challenge is that the many structure fragments stored at the PDB only provide a partial view because full-length proteins are flexible entities and dynamics play a key role in their functionality. Another challenge is the shortage of high-resolution data on functionally important higher-order complexes. Still, the interest in the structure/function relationships of EGFR remains unabated because of the crucial role played by oncogenic EGFR mutants in driving non-small cell lung cancer (NSCLC). Despite targeted therapies against EGFR setting a milestone in the treatment of this disease, ubiquitous drug resistance inevitably emerges after one year or so of treatment. The magnitude of the challenge has inspired novel strategies. Among these, the combination of multi-disciplinary experiments and molecular dynamic (MD) simulations have been pivotal in revealing the basic nature of EGFR monomers, dimers and multimers, and the structure-function relationships that underpin the mechanisms by which EGFR dysregulation contributes to the onset of NSCLC and resistance to treatment.

Nature's Shortcut to Protein Folding

This Feature Article presents a view of the protein folding transition based on the hypothesis that Nature has built features within the sequences that enable a Shortcut to efficient folding. Nature's Shortcut is proposed to be the early establishment of a set of nonlocal weak contacts, constituting protein loops that significantly constrain regions of the collapsed disordered protein into a native-like low-resolution fluctuating topology of major sections of the backbone. Nature's establishment of this scaffold of nonlocal contacts is claimed to bypass what would otherwise be a nearly hopeless unaided search for the final three-dimensional structure in proteins longer than ∼100 amino acids. To support this main contention of the Feature Article, the loop hypothesis (LH) description of early folding events is experimentally tested with time-resolved Förster resonance energy transfer techniques for adenylate kinase, and the data are shown to be consistent with theoretical predictions from the sequential collapse model (SCM). The experimentally based LH and the theoretically founded SCM are argued to provide a unified picture of the role of nonlocal contacts as constituting Nature's Shortcut to protein folding. Importantly, the SCM is shown to reliably predict key nonlocal contacts utilizing only primary sequence information. This view on Nature's Shortcut is open to the protein community for further detailed assessment, including its practical consequences, by suitable application of advanced experimental and computational techniques.