FACTS: Fast analytical continuum treatment of solvation

Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains

The current practical "workhorses" of the atomistic implicit solvation─the Poisson-Boltzmann (PB) and generalized Born (GB) models─face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE ∼ 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.

Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment

The accuracy of computational models of water is key to atomistic simulations of biomolecules. We propose a computationally efficient way to improve the accuracy of the prediction of hydration-free energies (HFEs) of small molecules: the remaining errors of the physics-based models relative to the experiment are predicted and mitigated by machine learning (ML) as a postprocessing step. Specifically, the trained graph convolutional neural network attempts to identify the "blind spots" in the physics-based model predictions, where the complex physics of aqueous solvation is poorly accounted for, and partially corrects for them. The strategy is explored for five classical solvent models representing various accuracy/speed trade-offs, from the fast analytical generalized Born (GB) to the popular TIP3P explicit solvent model; experimental HFEs of small neutral molecules from the FreeSolv set are used for the training and testing. For all of the models, the ML correction reduces the resulting root-mean-square error relative to the experiment for HFEs of small molecules, without significant overfitting and with negligible computational overhead. For example, on the test set, the relative accuracy improvement is 47% for the fast analytical GB, making it, after the ML correction, almost as accurate as uncorrected TIP3P. For the TIP3P model, the accuracy improvement is about 39%, bringing the ML-corrected model's accuracy below the 1 kcal/mol threshold. In general, the relative benefit of the ML corrections is smaller for more accurate physics-based models, reaching the lower limit of about 20% relative accuracy gain compared with that of the physics-based treatment alone. The proposed strategy of using ML to learn the remaining error of physics-based models offers a distinct advantage over training ML alone directly on reference HFEs: it preserves the correct overall trend, even well outside of the training set.

Asymmetric framework motion of TCRαβ controls load-dependent peptide discrimination

Mechanical force is critical for the interaction between an αβ T cell receptor (TCR) and a peptide-bound major histocompatibility complex (pMHC) molecule to initiate productive T-cell activation. However, the underlying mechanism remains unclear. We use all-atom molecular dynamics simulations to examine the A6 TCR bound to HLA-A*02:01 presenting agonist or antagonist peptides under different extensions to simulate the effects of applied load on the complex, elucidating their divergent biological responses. We found that TCR α and β chains move asymmetrically, which impacts the interface with pMHC, in particular the peptide-sensing CDR3 loops. For the wild-type agonist, the complex stabilizes in a load-dependent manner while antagonists destabilize it. Simulations of the Cβ FG-loop deletion, which reduces the catch bond response, and simulations with in silico mutant peptides further support the observed behaviors. The present results highlight the combined role of interdomain motion, fluctuating forces, and interfacial contacts in determining the mechanical response and fine peptide discrimination by a TCR, thereby resolving the conundrum of nearly identical crystal structures of TCRαβ-pMHC agonist and antagonist complexes.

3-O-Methyltolcapone and Its Lipophilic Analogues Are Potent Inhibitors of Transthyretin Amyloidogenesis with High Permeability and Low Toxicity

Transthyretin (TTR) is an amyloidogenic homotetramer involved in the transport of thyroxine in blood and cerebrospinal fluid. To date, more than 130 TTR point mutations are known to destabilise the TTR tetramer, leading to its extracellular pathological aggregation accumulating in several organs, such as heart, peripheral and autonomic nerves, and leptomeninges. Tolcapone is an FDA-approved drug for Parkinson's disease that has been repurposed as a TTR stabiliser. We characterised 3-O-methyltolcapone and two newly synthesized lipophilic analogues, which are expected to be protected from the metabolic glucuronidation that is responsible for the lability of tolcapone in the organism. Immunoblotting assays indicated the high degree of TTR stabilisation, coupled with binding selectivity towards TTR in diluted plasma of 3-O-methyltolcapone and its lipophilic analogues. Furthermore, in vitro toxicity data showed their several-fold improved neuronal and hepatic safety compared to tolcapone. Calorimetric and structural data showed that both T4 binding sites of TTR are occupied by 3-O-methyltolcapone and its lipophilic analogs, consistent with an effective TTR tetramer stabilisation. Moreover, in vitro permeability studies showed that the three compounds can effectively cross the blood-brain barrier, which is a prerequisite for the inhibition of TTR amyloidogenesis in the cerebrospinal fluid. Our data demonstrate the relevance of 3-O-methyltolcapone and its lipophilic analogs as potent inhibitors of TTR amyloidogenesis.

Two-Step Covalent Docking with Attracting Cavities

Due to their various advantages, interest in the development of covalent drugs has been renewed in the past few years. It is therefore important to accurately describe and predict their interactions with biological targets by computer-aided drug design tools such as docking algorithms. Here, we report a covalent docking procedure for our in-house docking code Attracting Cavities (AC), which mimics the two-step mechanism of covalent ligand binding. Ligand binding to the protein cavity is driven by nonbonded interactions, followed by the formation of a covalent bond between the ligand and the protein through a chemical reaction. To test the performance of this method, we developed a diverse, high-quality, openly accessible re-docking benchmark set of 95 covalent complexes bound by 8 chemical reactions to 5 different reactive amino acids. Combination with structures from previous studies resulted in a set of 304 complexes, on which AC obtained a success rate (rmsd ≤ 2 Å) of 78%, outperforming two state-of-the-art covalent docking codes, genetic optimization for ligand docking (GOLD (66%)) and AutoDock (AD (35%)). Using a more stringent success criterion (rmsd ≤ 1.5 Å), AC reached a success rate of 71 vs 55% for GOLD and 26% for AD. We additionally assessed the cross-docking performance of AC on a set of 76 covalent complexes of the SARS-CoV-2 main protease. On this challenging test set of mainly small and highly solvent-exposed ligands, AC yielded success rates of 58 and 28% for re-docking and cross-docking, respectively, compared to 45 and 17% for GOLD.

FASTDock: A Pipeline for Allosteric Drug Discovery

Allostery is involved in innumerable biological processes and plays a fundamental role in human disease. Thus, the exploration of allosteric modulation is crucial for research on biological mechanisms and in the development of novel therapeutics. The development of small-molecule allosteric effectors can be used as tools to probe biological mechanisms of interest. One of the main limitations in targeting allosteric sites is the difficulty in uncovering them for specific receptors. Furthermore, upon discovery of novel allosteric modulation, early lead generation is made more difficult as compared to that at orthosteric sites because there is likely no information about the types of molecules that can bind at the site. In the work described here, we present a novel drug discovery pipeline, FASTDock, which allows one to uncover ligandable sites as well as small molecules that target the given site without requiring pre-existing knowledge of ligands that can bind in the targeted site. By using a hierarchical screening strategy, this method has the potential to enable high-throughput screens of an exceptionally large database of targeted ligand space.

New Cyclopiane Diterpenes and Polyketide Derivatives from Marine Sediment-Derived Fungus Penicillium antarcticum KMM 4670 and Their Biological Activities

Two new cyclopiane diterpenes and a new cladosporin precursor, together with four known related compounds, were isolated from the marine sediment-derived fungus Penicillium antarcticum KMM 4670, which was re-identified based on phylogenetic inference from ITS, BenA, CaM, and RPB2 gene regions. The absolute stereostructures of the isolated cyclopianes were determined using modified Mosher's method and quantum chemical calculations of the ECD spectra. The isolation from the natural source of two biosynthetic precursors of cladosporin from a natural source has been reported for the first time. The antimicrobial activities of the isolated compounds against Staphylococcus aureus, Escherichia coli, and Candida albicans as well as the inhibition of staphylococcal sortase A activity were investigated. Moreover, the cytotoxicity of the compounds to mammalian cardiomyocytes H9c2 was studied. As a result, new cyclopiane diterpene 13-epi-conidiogenone F was found to be a sortase A inhibitor and a promising anti-staphylococcal agent.

Developing similarity matrices for antibody-protein binding interactions

The inventions of AlphaFold and RoseTTAFold are revolutionizing computational protein science due to their abilities to reliably predict protein structures. Their unprecedented successes are due to the parallel consideration of several types of information, one of which is protein sequence similarity information. Sequence homology has been studied for many decades and depends on similarity matrices to define how similar or different protein sequences are to one another. A natural extension of predicting protein structures is predicting the interactions between proteins, but similarity matrices for protein-protein interactions do not exist. This study conducted a mutational analysis of 384 non-redundant antibody-protein antigen complexes to calculate antibody-protein interaction similarity matrices. Every important residue in each antibody and each antigen was mutated to each of the other 19 commonly occurring amino acids and the percentage changes in interaction energies were calculated using three force fields: CHARMM, Amber, and Rosetta. The data were used to construct six interaction similarity matrices, one for antibodies and another for antigens using each force field. The matrices exhibited both commonalities, such as mutations of aromatic and charged residues being the most detrimental, and differences, such as Rosetta predicting mutations of serines to be better tolerated than either Amber or CHARMM. A comparison to nine previously published similarity matrices for protein sequences revealed that the new interaction matrices are more similar to one another than they are to any of the previous matrices. The created similarity matrices can be used in force field specific applications to help guide decisions regarding mutations in protein-protein binding interfaces.

1-Piperidine Propionic Acid as an Allosteric Inhibitor of Protease Activated Receptor-2

In the last decades, studies on the inflammatory signaling pathways in multiple pathological contexts have revealed new targets for novel therapies. Among the family of G-protein-coupled Proteases Activated Receptors, PAR2 was identified as a driver of the inflammatory cascade in many pathologies, ranging from autoimmune disease to cancer metastasis. For this reason, many efforts have been focused on the development of potential antagonists of PAR2 activity. This work focuses on a small molecule, 1-Piperidine Propionic Acid (1-PPA), previously described to be active against inflammatory processes, but whose target is still unknown. Stabilization effects observed by cellular thermal shift assay coupled to in-silico investigations, including molecular docking and molecular dynamics simulations, suggested that 1-PPA binds PAR2 in an allosteric pocket of the receptor inactive conformation. Functional studies revealed the antagonist effects on MAPKs signaling and on platelet aggregation, processes mediated by PAR family members, including PAR2. Since the allosteric pocket binding 1-PPA is highly conserved in all the members of the PAR family, the evidence reported here suggests that 1-PPA could represent a promising new small molecule targeting PARs with antagonistic activity.

Asymmetric framework motion of TCRαβ controls load-dependent peptide discrimination

Mechanical force is critical for the interaction between an αβT cell receptor (TCR) and a peptide-bound major histocompatibility complex (pMHC) molecule to initiate productive T-cell activation. However, the underlying mechanism remains unclear. We use all-atom molecular dynamics simulations to examine the A6 TCR bound to HLA-A*02:01 presenting agonist or antagonist peptides under different extensions to simulate the effects of applied load on the complex, elucidating their divergent biological responses. We found that TCR α and β chains move asymmetrically, which impacts the interface with pMHC, in particular the peptide-sensing CDR3 loops. For the wild-type agonist, the complex stabilizes in a load-dependent manner while antagonists destabilize it. Simulations of the Cβ FG-loop deletion, which reduces the catch bond response, and simulations with in silico mutant peptides further support the observed behaviors. The present results highlight the combined role of interdomain motion, fluctuating forces, and interfacial contacts in determining the mechanical response and fine peptide discrimination by a TCR, thereby resolving the conundrum of nearly identical crystal structures of TCRαβ-pMHC agonist and antagonist complexes.

Anthraquinone Derivatives and Other Aromatic Compounds from Marine Fungus Asteromyces cruciatus KMM 4696 and Their Effects against Staphylococcus aureus

New anthraquinone derivatives acruciquinones A-C (1-3), together with ten known metabolites, were isolated from the obligate marine fungus Asteromyces cruciatus KMM 4696. Acruciquinone C is the first member of anthraquinone derivatives with a 6/6/5 backbone. The structures of isolated compounds were established based on NMR and MS data. The absolute stereoconfigurations of new acruciquinones A-C were determined using ECD and quantum chemical calculations (TDDFT approach). A plausible biosynthetic pathway of the novel acruciquinone C was proposed. Compounds 1-4 and 6-13 showed a significant antimicrobial effects against Staphylococcus aureus growth, and acruciquinone A (1), dendryol B (4), coniothyrinone B (7), and ω-hydroxypachybasin (9) reduced the activity of a key staphylococcal enzyme, sortase A. Moreover, the compounds, excluding 4, inhibited urease activity. We studied the effects of anthraquinones 1, 4, 7, and 9 and coniothyrinone D (6) in an in vitro model of skin infection when HaCaT keratinocytes were cocultivated with S. aureus. Anthraquinones significantly reduce the negative impact of S. aureus on the viability, migration, and proliferation of infected HaCaT keratinocytes, and acruciquinone A (1) revealed the most pronounced effect.

Attracting Cavities 2.0: Improving the Flexibility and Robustness for Small-Molecule Docking

Molecular docking is a computational approach for predicting the most probable position of a ligand in the binding site of a target macromolecule. Our docking algorithm Attracting Cavities (AC) has been shown to compare favorably to other widely used docking algorithms [Zoete, V.; et al. J. Comput. Chem. 2016, 37, 437]. Here we describe several improvements of AC, making the sampling more robust and providing more flexibility for either fast or high-accuracy docking. We benchmark the performance of AC 2.0 using the 285 complexes of the PDBbind Core set, version 2016. For redocking from randomized ligand conformations, AC 2.0 reaches a success rate of 73.3%, compared to 63.9% for GOLD and 58.0% for AutoDock Vina. Due to its force-field-based scoring function and its thorough sampling procedure, AC 2.0 also performs well for blind docking on the entire receptor surface. The accuracy of its scoring function allows for the detection of problematic experimental structures in the benchmark set. For cross-docking, the AC 2.0 success rate is about 30% lower than for redocking (42.5%), similar to GOLD (42.8%) and better than AutoDock Vina (33.1%), and it can be improved by an informed choice of flexible protein residues. For selected targets with a high success rate in cross-docking, AC 2.0 also achieves good enrichment factors in virtual screening.

Membrane Cholesterol Inhibits Progesterone-Mediated Sperm Function through the Possible Involvement of ABHD2

Abhydrolase domain containing 2-acylglycerol lipase (ABHD2) was recently claimed as the membrane receptor of progesterone (P4) in sperm cells, mediating cell processes such as sperm chemotaxis and acrosome reaction. Here, we investigated the role of membrane cholesterol (Chol) on ABHD2-mediated human sperm chemotaxis. Human sperm cells were obtained from twelve normozoospemic healthy donors. ABHD2-Chol interaction was modelled by computational molecular-modelling (MM). Sperm membrane Chol content was depleted by incubating cells with cyclodextrin (CD) or augmented by the incubation with the complex between CD and Chol (CD:Chol). Cell Chol levels were quantified by liquid chromatography-mass spectrometry. Sperm migration upon P4 gradient was evaluated through the accumulation assay in a specific migration device. Motility parameters were evaluated by sperm class analyzer, whilst intracellular calcium concentration, acrosome reaction and mitochondrial membrane potential were evaluated with calcium orange, FITC-conjugated anti-CD46 antibody and JC-1 fluorescent probes, respectively. MM analysis showed the possible stable binding Chol to ABHD2, resulting in to major impact on the protein backbone flexibility. The treatment with CD was associated with a dose-dependent increase in sperm migration in a 160 nM P4 gradient, together with increase in sperm motility parameters and levels of acrosome reaction. The treatment with CD:Chol was associated with essentially opposite effects. Chol was, thus, suggested to inhibit P4-mediated sperm function through the possible inhibition of ABHD2.

Recombinant expression and characterization of GSTd3 from a resistant population of Anopheles arabiensis and comparison of DDTase activity with GSTe2

The development of insecticide resistance in malaria vectors is a challenge for the global effort to control and eradicate malaria. Glutathione S-transferases (GSTs) are multifunctional enzymes involved in the detoxification of many classes of insecticides. For mosquitoes, it is known that overexpression of an epsilon GST, GSTe2, confers resistance towards DDT and pyrethroids. In addition to GSTe2, consistent overexpression of a delta class GST, GSTd3, has been observed in insecticide resistant populations of different malaria vector species. However, the functional role of GSTd3 towards DDT resistance has not yet been investigated. Here, we recombinantly expressed both GSTe2 and GSTd3 from Anopheles arabiensis and compared their metabolic activities against DDT. Both AaGSTd3 and AaGSTe2 exhibited CDNB-conjugating and glutathione peroxidase activity and DDT metabolism was observed for both GSTs. However, the DDT dehydrochlorinase activity exhibited by AaGSTe2 was much higher than for AaGSTd3, and AaGSTe2 was also able to eliminate DDE although the metabolite could not be identified. Molecular modeling revealed subtle differences in the binding pocket of both enzymes and a better fit of DDT within the H-site of AaGSTe2. The overexpression but much lower DDT metabolic activity of AaGSTd3, might suggest that AaGSTd3 sequesters DDT. These findings highlight the complexity of insecticide resistance in the major malaria vectors and the difficulties associated with control of the vectors using DDT, which is still used for indoor residual spraying.

Computational studies of potential antiviral compounds from some selected Nigerian medicinal plants against SARS-CoV-2 proteins

The challenges posed by COVID-19's emergence have led to a search for its therapies. There is no cure for COVID-19 infection yet, but there is significant progress in vaccine formulation for prophylaxis and drug development (such as paxlovid) for high-risk patients. As a contribution to the ongoing quest for solutions, this study shows potent phytocompounds identification as inhibitors of SARS-CoV-2 targets using in silico methods. We used virtual screening, molecular docking, and molecular dynamics (MD) simulations to investigate the interaction of some phytochemicals with 3CLpro, ACE2, and PLpro proteins crucial to the SARS-CoV-2 viral cycle. The predicted docking scores range from −5.5 to −9.4 kcal/mol, denoting appreciable binding of these compounds to the SARS-CoV-2 proteins and presenting a multitarget inhibition for COVID-19. Some phytocompounds interact favorably at non-active sites of the enzymes. For instance, MD simulation shows that an identified site on PLpro is stable and likely an allosteric region for inhibitor binding and modulation. These phytocompounds could be developed into effective therapy against COVID-19 and probed as potential multitarget-directed ligands and drug candidates against the SARS-CoV-2 virus. The study unveils drug repurposing, selectivity, allosteric site targeting, and multitarget-directed ligand in one piece. These concepts are three distinct approaches in the drug design and discovery pipeline.

Structure-based optimization of type III indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors

The haem enzyme indoleamine 2,3-dioxygenase 1 (IDO1) catalyses the rate-limiting step in the kynurenine pathway of tryptophan metabolism and plays an essential role in immunity, neuronal function, and ageing. Expression of IDO1 in cancer cells results in the suppression of an immune response, and therefore IDO1 inhibitors have been developed for use in anti-cancer immunotherapy. Here, we report an extension of our previously described highly efficient haem-binding 1,2,3-triazole and 1,2,4-triazole inhibitor series, the best compound having both enzymatic and cellular IC₅₀ values of 34 nM. We provide enzymatic inhibition data for almost 100 new compounds and X-ray diffraction data for one compound in complex with IDO1. Structural and computational studies explain the dramatic drop in activity upon extension to pocket B, which has been observed in diverse haem-binding inhibitor scaffolds. Our data provides important insights for future IDO1 inhibitor design.

In-silico probing of AML related RUNX1 cancer-associated missense mutations: Predicted relationships to DNA binding and drug interactions

The molecular consequences of cancer associated mutations in Acute myeloid leukemia (AML) linked factors are not very well understood. Here, we interrogated the COSMIC database for missense mutations associated with the RUNX1 protein, that is frequently mis-regulated in AML, where we sought to identify recurrently mutated positions at the DNA-interacting interface. Indeed, six of the mutated residues, out of a total 417 residues examined within the DNA binding domain, evidenced reduced DNA association in in silico predictions. Further, given the prominence of RUNX1’s compromised function in AML, we asked the question if the mutations themselves might alter RUNX1’s interaction (off-target) with known FDA-approved drug molecules, including three currently used in treating AML. We identified several AML-associated mutations in RUNX1 that were calculated to enhance RUNX1’s interaction with specific drugs. Specifically, we retrieved data from the COSMIC database for cancer-associated mutations of RUNX1 by using R package “data.table” and “ggplot2” modules. In the presence of DNA and/or drug, we used docking scores and energetics of the complexes as tools to evaluate predicted interaction strengths with RUNX1. For example, we performed predictions of drug binding pockets involving Enasidenib, Giltertinib, and Midostaurin (AML associated), as well as ten different published cancer associated drug compounds. Docking of wild type RUNX1 with these 13 different cancer-associated drugs indicates that wild-type RUNX1 has a lower efficiency of binding while RUNX1 mutants R142K, D171N, R174Q, P176H, and R177Q suggested higher affinity of drug association. Literature evidence support our prediction and suggests the mutation R174Q affects RUNX1 DNA binding and could lead to compromised function. We conclude that specific RUNX1 mutations that lessen DNA binding facilitate the binding of a number of tested drug molecules. Further, we propose that molecular modeling and docking studies for RUNX1 in the presence of DNA and/or drugs enables evaluation of the potential impact of RUNX1 cancer associated mutations in AML.

ppdx: Automated modeling of protein-protein interaction descriptors for use with machine learning

This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein-protein interactions. The descriptors can be used to predict various properties of interest, such as protein-protein binding affinities, or inhibitory concentrations (IC₅₀ ), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC₅₀ of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk.

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.

A Closed-Form, Analytical Approximation for Apparent Surface Charge and Electric Field of Molecules

Closed-form, analytical approximations for electrostatic properties of molecules are of unique value as these can provide computational speed, versatility, and physical insight. Here, we have derived a simple, closed-form formula for the apparent surface charge (ASC) as well as for the electric field generated by a molecular charge distribution in aqueous solution. The approximation, with no fitted parameters, was tested against numerical solutions of the Poisson equation, where it has produced a significant speed-up. For neutral small molecules, the hydration free energies estimated from the closed-form ASC formula are within 0.8 kcal/mol RMSD from the numerical Poisson reference; the electric field at the surface is in quantitative agreement with the reference. Performance of the approximation was also tested on larger structures, including a protein, a DNA fragment, and a viral receptor-target complex. For all structures tested, a near-quantitative agreement with the numerical Poisson reference was achieved, except in regions of high negative curvature, where the new approximation is still qualitatively correct. A unique efficiency feature of the proposed "source-based″ closed-form approximation is that the ASC and electric field can be estimated individually at any point or surface patch, without the need to obtain the full global solution. An open-source software implementation of the method is available: https://people.cs.vt.edu/~onufriev/CODES/aasc.zip.

Covalent docking in CDOCKER

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

Cytosolic microRNA-inducible nuclear translocation of Cas9 protein for disease-specific genome modification

MicroRNA-dependent mRNA decay plays an important role in gene silencing by facilitating posttranscriptional and translational repression. Inspired by this intrinsic nature of microRNA-mediated mRNA cleavage, here, we describe a microRNA-targeting mRNA as a switch platform called mRNA bridge mimetics to regulate the translocation of proteins. We applied the mRNA bridge mimetics platform to Cas9 protein to confer it the ability to translocate into the nucleus via cleavage of the nuclear export signal. This system performed programmed gene editing in vitro and in vivo. Combinatorial treatment with cisplatin and miR-21-EZH2 axis-targeting CRISPR Self Check-In improved sensitivity to chemotherapeutic drugs in vivo. Using the endogenous microRNA-mediated mRNA decay mechanism, our platform is able to remodel a cell's natural biology to allow the entry of precise drugs into the nucleus, devoid of non-specific translocation. The mRNA bridge mimetics strategy is promising for applications in which the reaction must be controlled via intracellular stimuli and modulates Cas9 proteins to ensure safe genome modification in diseased conditions.

Benchmarking of structure refinement methods for protein complex models

Protein structure docking is the process in which the quaternary structure of a protein complex is predicted from individual tertiary structures of the protein subunits. Protein docking is typically performed in two main steps. The subunits are first docked while keeping them rigid to form the complex, which is then followed by structure refinement. Structure refinement is crucial for a practical use of computational protein docking models, as it is aimed for correcting conformations of interacting residues and atoms at the interface. Here, we benchmarked the performance of eight existing protein structure refinement methods in refinement of protein complex models. We show that the fraction of native contacts between subunits is by far the most straightforward metric to improve. However, backbone dependent metrics, based on the Root Mean Square Deviation proved more difficult to improve via refinement.

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy

The binding of small-molecule ligands to protein or nucleic acid targets is important to numerous biological processes. Accurate prediction of the binding modes between a ligand and a macromolecule is of fundamental importance in structure-based structure-function exploration. When multiple ligands with different sizes are docked to a target receptor, it is reasonable to assume that the residues in the binding pocket may adopt alternative conformations upon interacting with the different ligands. In addition, it has been suggested that the entropic contribution to binding can be important. However, only a few attempts to include the side chain conformational entropy upon binding within the application of flexible receptor docking methodology exist. Here, we propose a new physics-based scoring function that includes both enthalpic and entropic contributions upon binding by considering the conformational variability of the flexible side chains within the ensemble of docked poses. We also describe a novel hybrid searching algorithm that combines both molecular dynamics (MD)-based simulated annealing and genetic algorithm crossovers to address the enhanced sampling of the increased search space. We demonstrate improved accuracy in flexible cross-docking experiments compared with rigid cross-docking. We test our developments by considering five protein targets, thrombin, dihydrofolate reductase(DHFR), T4 L99A, T4 L99A/M102Q, and PDE10A, which belong to different enzyme classes with different binding pocket environments, as a representative set of diverse ligands and receptors. Each target contains dozens of different ligands bound to the same binding pocket. We also demonstrate that this flexible docking algorithm may be applicable to RNA docking with a representative riboswitch example. Our findings show significant improvements in top ranking accuracy across this set, with the largest improvement relative to rigid, 23.64%, occurring for ligands binding to DHFR. We then evaluate the ability to identify lead compounds among a large chemical space for the proposed flexible receptor docking algorithm using a subset of the DUD-E containing receptor targets MCR, GCR, and ANDR. We demonstrate that our new algorithms show improved performance in modeling flexible binding site residues compared to DOCK. Finally, we select the T4 L99A and T4 L99A/M102Q decoy sets, containing dozens of binders and experimentally validated nonbinders, to test our approach in distinguishing binders from nonbinders. We illustrate that our new algorithms for searching and scoring have superior performance to rigid receptor CDOCKER as well as AutoDock Vina. Finally, we suggest that flexible CDOCKER is sufficiently fast to be utilized in high-throughput docking screens in the context of hierarchical approaches.

Locating ligand binding sites in G-protein coupled receptors using combined information from docking and sequence conservation

GPCRs (G-protein coupled receptors) are the largest family of drug targets and share a conserved structure. Binding sites are unknown for many important GPCR ligands due to the difficulties of GPCR recombinant expression, biochemistry, and crystallography. We describe our approach, ConDockSite, for predicting ligand binding sites in class A GPCRs using combined information from surface conservation and docking, starting from crystal structures or homology models. We demonstrate the effectiveness of ConDockSite on crystallized class A GPCRs such as the beta2 adrenergic and A2A adenosine receptors. We also demonstrate that ConDockSite successfully predicts ligand binding sites from high-quality homology models. Finally, we apply ConDockSite to predict the ligand binding sites on a structurally uncharacterized GPCR, GPER, the G-protein coupled estrogen receptor. Most of the sites predicted by ConDockSite match those found in other independent modeling studies. ConDockSite predicts that four ligands bind to a common location on GPER at a site deep in the receptor cleft. Incorporating sequence conservation information in ConDockSite overcomes errors introduced from physics-based scoring functions and homology modeling.

Advanced Sampling Methods for Multiscale Simulation of Disordered Proteins and Dynamic Interactions

Intrinsically disordered proteins (IDPs) are highly prevalent and play important roles in biology and human diseases. It is now also recognized that many IDPs remain dynamic even in specific complexes and functional assemblies. Computer simulations are essential for deriving a molecular description of the disordered protein ensembles and dynamic interactions for a mechanistic understanding of IDPs in biology, diseases, and therapeutics. Here, we provide an in-depth review of recent advances in the multi-scale simulation of disordered protein states, with a particular emphasis on the development and application of advanced sampling techniques for studying IDPs. These techniques are critical for adequate sampling of the manifold functionally relevant conformational spaces of IDPs. Together with dramatically improved protein force fields, these advanced simulation approaches have achieved substantial success and demonstrated significant promise towards the quantitative and predictive modeling of IDPs and their dynamic interactions. We will also discuss important challenges remaining in the atomistic simulation of larger systems and how various coarse-grained approaches may help to bridge the remaining gaps in the accessible time- and length-scales of IDP simulations.

GalaxyWater-wKGB: Prediction of Water Positions on Protein Structure Using wKGB Statistical Potential

Proteins fold and function in water, and protein-water interactions play important roles in protein structure and function. In computational studies on protein structure and interaction, the effect of water is considered either implicitly or explicitly. Implicit water models are frequently used in protein structure prediction and docking because they are computationally much more efficient than explicit water models, which are often employed in molecular dynamics (MD) simulations. However, implicit water models that treat water as a continuous solvent medium cannot account for specific atomistic protein-water interactions that are critical for structure formation and interactions with other molecules. Various methods for predicting water molecules that form specific atomistic interactions with proteins have been developed. Methods involving MD simulations or the integral equation theory tend to produce more accurate results at a higher computational cost than simple geometry- or energy-based methods. Here, we present a novel method for predicting water positions on a protein surface called GalaxyWater-wKGB, which is based on a statistical potential, a water knowledge-based potential based on the generalized Born model (wKGB). This method is accurate and rapid because it does not require conformational sampling or iterative computation owing to the effective statistical treatment employed to derive the potential. The statistical potential describes specific protein atom-water interactions more accurately than conventional potentials by considering the dependence on the degree of solvent accessibility of protein atoms as well as on protein atom-water distances and orientations. The introduction of solvent accessibility allows effective consideration of competing nonspecific protein-water and intraprotein interactions. When tested on high-resolution protein crystal structures, this method could recover similar or larger fractions of crystallographic water 180 times faster than the sophisticated integral equation theory, 3D-RISM. A web service of this water prediction method is freely available at http://galaxy.seoklab.org/wkgb.

TMPRSS2 inhibitor discovery facilitated through an in silico and biochemical screening platform

The COVID-19 pandemic has highlighted the need for new antiviral targets, as many of the currently approved drugs have proven ineffective against mitigating SARS-CoV-2 infections. The host transmembrane serine protease TMPRSS2 is a highly promising antiviral target, as it plays a direct role in priming the spike protein before viral entry occurs. Further, unlike other targets such as ACE2, TMPRSS2 has no known biological role. Here we utilize virtual screening to curate large libraries into a focused collection of potential inhibitors. Optimization of a recombinant expression and purification protocol for the TMPRSS2 peptidase domain facilitates subsequent biochemical screening and characterization of selected compounds from the curated collection in a kinetic assay. In doing so, we demonstrate that serine protease inhibitors camostat, nafamostat, and gabexate inhibit through a covalent mechanism. We further identify new non-covalent compounds as TMPRSS2 protease inhibitors, demonstrating the utility of a combined virtual and experimental screening campaign in rapid drug discovery efforts.

A single-chain and fast-responding light-inducible Cre recombinase as a novel optogenetic switch

Optogenetics enables genome manipulations with high spatiotemporal resolution, opening exciting possibilities for fundamental and applied biological research. Here, we report the development of LiCre, a novel light-inducible Cre recombinase. LiCre is made of a single flavin-containing protein comprising the AsLOV2 photoreceptor domain of Avena sativa fused to a Cre variant carrying destabilizing mutations in its N-terminal and C-terminal domains. LiCre can be activated within minutes of illumination with blue light without the need of additional chemicals. When compared to existing photoactivatable Cre recombinases based on two split units, LiCre displayed faster and stronger activation by light as well as a lower residual activity in the dark. LiCre was efficient both in yeast, where it allowed us to control the production of β-carotene with light, and human cells. Given its simplicity and performances, LiCre is particularly suited for fundamental and biomedical research, as well as for controlling industrial bioprocesses.

In a biologist’s toolkit, the Cre protein holds a special place. Naturally found in certain viruses, this enzyme recognises and modifies specific genetic sequences, creating changes that switch on or off whatever gene is close by. Genetically engineering cells or organisms so that they carry Cre and its target sequences allows scientists to control the activation of a given gene, often in a single tissue or organ.

However, this relies on the ability to activate the Cre protein ‘on demand’ once it is in the cells of interest. One way to do so is to split the enzyme into two pieces, which can then reassemble when exposed to blue light. Yet, this involves the challenging step of introducing both parts separately into a tissue.

Instead, Duplus-Bottin et al. engineered LiCre, a new system where a large section of the Cre protein is fused to a light sensor used by oats to detect their environment. LiCre is off in the dark, but it starts to recognize and modify Cre target sequences when exposed to blue light.

Duplus-Bottin et al. then assessed how LiCre compares to the two-part Cre system in baker's yeast and human kidney cells. This showed that the new protein is less ‘incorrectly’ active in the dark, and can switch on faster under blue light. The improved approach could give scientists a better tool to study the role of certain genes at precise locations and time points, but also help them to harness genetic sequences for industry or during gene therapy.

Qualitative Prediction of Ligand Dissociation Kinetics from Focal Adhesion Kinase Using Steered Molecular Dynamics

Most early-stage drug discovery projects focus on equilibrium binding affinity to the target alongside selectivity and other pharmaceutical properties. Since many approved drugs have nonequilibrium binding characteristics, there has been increasing interest in optimizing binding kinetics early in the drug discovery process. As focal adhesion kinase (FAK) is an important drug target, we examine whether steered molecular dynamics (SMD) can be useful for identifying drug candidates with the desired drug-binding kinetics. In simulating the dissociation of 14 ligands from FAK, we find an empirical power–law relationship between the simulated time needed for ligand unbinding and the experimental rate constant for dissociation, with a strong correlation depending on the SMD force used. To improve predictions, we further develop regression models connecting experimental dissociation rate with various structural and energetic quantities derived from the simulations. These models can be used to predict dissociation rates from FAK for related compounds.

Amyloid-Like Peptide Aggregates

Self-assembly of proteins and peptides into the amyloid fold is a widespread phenomenon in the natural world. The structural hallmark of self-assembly into amyloid fibrillar assemblies is the cross-beta motif, which conveys distinct morphological and mechanical properties. The amyloid fibril formation has contrasting results depending on the organism, in the sense that it can bestow an organism with the advantages of mechanical strength and improved functionality or, on the contrary, could give rise to pathological states. In this chapter we review the existing information on amyloid-like peptide aggregates, which could either be derived from protein sequences, but also could be rationally or de novo designed in order to self-assemble into amyloid fibrils under physiological conditions. Moreover, the development of self-assembled fibrillar biomaterials that are tailored for the desired properties towards applications in biomedical or environmental areas is extensively analyzed. We also review computational studies predicting the amyloid propensity of the natural amino acid sequences and the structure of amyloids, as well as designing novel functional amyloid materials.

Generalized Born radii computation using linear models and neural networks

MOTIVATION

Implicit solvent models play an important role in describing the thermodynamics and the dynamics of biomolecular systems. Key to an efficient use of these models is the computation of generalized Born (GB) radii, which is accomplished by algorithms based on the electrostatics of inhomogeneous dielectric media. The speed and accuracy of such computations are still an issue especially for their intensive use in classical molecular dynamics. Here, we propose an alternative approach that encodes the physics of the phenomena and the chemical structure of the molecules in model parameters which are learned from examples.

RESULTS

GB radii have been computed using (i) a linear model and (ii) a neural network. The input is the element, the histogram of counts of neighbouring atoms, divided by atom element, within 16 Å. Linear models are ca. 8 times faster than the most widely used reference method and the accuracy is higher with correlation coefficient with the inverse of 'perfect' GB radii of 0.94 versus 0.80 of the reference method. Neural networks further improve the accuracy of the predictions with correlation coefficient with 'perfect' GB radii of 0.97 and ca. 20% smaller root mean square error.

AVAILABILITY AND IMPLEMENTATION

We provide a C program implementing the computation using the linear model, including the coefficients appropriate for the set of Bondi radii, as Supplementary Material. We also provide a Python implementation of the neural network model with parameter and example files in the Supplementary Material as well.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

What Next for Quantum Mechanics in Structure-Based Drug Discovery?

There is significant potential for electronic structure methods to improve the quality of the predictions furnished by the tools of computer-aided drug design, which typically rely on empirically derived functions. In this perspective, we consider some recent examples of how quantum mechanics has been applied in predicting protein-ligand geometries, protein-ligand binding affinities and ligand strain on binding. We then outline several significant developments in quantum mechanics methodology likely to influence these approaches: in particular, we note the advent of more computationally expedient ab initio quantum mechanical methods that can provide chemical accuracy for larger molecular systems than hitherto possible. We highlight the emergence of increasingly accurate semiempirical quantum mechanical methods and the associated role of machine learning and molecular databases in their development. Indeed, the convergence of improved algorithms for solving and analyzing electronic structure, modern machine learning methods, and increasingly comprehensive benchmark data sets of molecular geometries and energies provides a context in which the potential of quantum mechanics will be increasingly realized in driving future developments and applications in structure-based drug discovery.

Accelerating the Generalized Born with Molecular Volume and Solvent Accessible Surface Area Implicit Solvent Model Using Graphics Processing Units

The generalized Born with molecular volume and solvent accessible surface area (GBMV2/SA) implicit solvent model provides an accurate description of molecular volume and has the potential to accurately describe the conformational equilibria of structured and disordered proteins. However, its broader application has been limited by the computational cost and poor scaling in parallel computing. Here, we report an efficient implementation of both the electrostatic and nonpolar components of GBMV2/SA on graphics processing unit (GPU) within the CHARMM/OpenMM module. The GPU-GBMV2/SA is numerically equivalent to the original CPU-GBMV2/SA. The GPU acceleration offers ~60- to 70-fold speedup on a single NVIDIA TITAN X (Pascal) graphics card for molecular dynamic simulations of both folded and unstructured proteins of various sizes. The current implementation can be further optimized to achieve even greater acceleration with minimal reduction on the numerical accuracy. The successful development of GPU-GBMV2/SA greatly facilitates its application to biomolecular simulations and paves the way for further development of the implicit solvent methodology. © 2019 Wiley Periodicals, Inc.

Designer Amyloid Cell-Penetrating Peptides for Potential Use as Gene Transfer Vehicles

Cell-penetrating peptides are used extensively to deliver molecules into cells due to their unique characteristics such as rapid internalization, charge, and non-cytotoxicity. Amyloid fibril biomaterials were reported as gene transfer or retroviral infection enhancers; no cell internalization of the peptides themselves is reported so far. In this study, we focus on two rationally and computationally designed peptides comprised of β-sheet cores derived from naturally occurring protein sequences and designed positively charged and aromatic residues exposed at key residue positions. The β-sheet cores bestow the designed peptides with the ability to self-assemble into amyloid fibrils. The introduction of positively charged and aromatic residues additionally promotes DNA condensation and cell internalization by the self-assembled material formed by the designed peptides. Our results demonstrate that these designer peptide fibrils can efficiently enter mammalian cells while carrying packaged luciferase-encoding plasmid DNA, and they can act as a protein expression enhancer. Interestingly, the peptides additionally exhibited strong antimicrobial activity against the enterobacterium Escherichia coli.

Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46

We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.

Revisiting the conformational state of albumin conjugated to gold nanoclusters: A self-assembly pathway to giant superstructures unraveled

Bovine serum albumin (BSA) is often employed as a proteinaceous component for synthesis of luminescent protein-stabilized gold nanoclusters (AuNC): intriguing systems with many potential applications. Typically, the formation of BSA-AuNC conjugate occurs under strongly alkaline conditions. Due to the sheer complexity of intertwined chemical and structural transitions taking place upon BSA-AuNC formation, the state of albumin enveloping AuNCs remains poorly characterized. Here, we study the conformational properties of BSA bound to AuNCs using an array of biophysical tools including vibrational spectroscopy, circular dichroism, fluorescence spectroscopy and trypsin digestion. The alkaline conditions of BSA-AuNC self-assembly appear to be primary responsible for the profound irreversible disruption of tertiary contacts, partial unfolding of native α-helices, hydrolysis of disulfide bonds and the protein becoming vulnerable to trypsin digestion. Further unfolding of BSA-AuNC by guanidinium hydrochloride (GdnHCl) is fully reversible equally in terms of albumin's secondary structure and conjugate's luminescent properties. This suggests that binding to AuNCs traps the albumin molecule in a state that is both partly disordered and refractory to irreversible misfolding. Indeed, when BSA-AuNC is subjected to conditions favoring self-association of BSA into amyloid-like fibrils, the buildup of non-native β-sheet conformation is less pronounced than in a control experiment with unmodified BSA. Unexpectedly, BSA-AuNC reveals a tendency to self-assemble into giant twisted superstructures of micrometer lengths detectable with transmission electron microscopy (TEM), a property absent in unmodified BSA. The process is accompanied by ordering of bound AuNCs into elongated streaks and simultaneous decrease in fluorescence intensity. The newly discovered self-association pathway appears to be specifically accessible to protein molecules with a certain restriction on structural dynamics which in the case of BSA-AuNC arises from binding to metal nanoclusters. Our results have been discussed in the context of mechanisms of protein misfolding and applications of BSA-AuNC.

GalaxyHomomer: a web server for protein homo-oligomer structure prediction from a monomer sequence or structure

Homo-oligomerization of proteins is abundant in nature, and is often intimately related with the physiological functions of proteins, such as in metabolism, signal transduction or immunity. Information on the homo-oligomer structure is therefore important to obtain a molecular-level understanding of protein functions and their regulation. Currently available web servers predict protein homo-oligomer structures either by template-based modeling using homo-oligomer templates selected from the protein structure database or by ab initio docking of monomer structures resolved by experiment or predicted by computation. The GalaxyHomomer server, freely accessible at http://galaxy.seoklab.org/homomer, carries out template-based modeling, ab initio docking or both depending on the availability of proper oligomer templates. It also incorporates recently developed model refinement methods that can consistently improve model quality. Moreover, the server provides additional options that can be chosen by the user depending on the availability of information on the monomer structure, oligomeric state and locations of unreliable/flexible loops or termini. The performance of the server was better than or comparable to that of other available methods when tested on benchmark sets and in a recent CASP performed in a blind fashion.

Inhibition of human 3-hydroxy-3-methylglutaryl CoA reductase by peptides leading to cholesterol homeostasis through SREBP2 pathway in HepG2 cells

In mammalian cells, human 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), a rate-limiting endoplasmic reticulum (ER) bonded enzyme, plays a central role in the cholesterol homeostasis via the negative feedback mechanism. The present study indicates that the interactions of novel peptides with the catalytic domain of HMGCR, provides an alternative therapeutic candidate for reducing cholesterol. The potential natural origin of HMGCR peptide inhibitors were filtered from the peptide library using the molecular docking, which revealed three strong candidates for inhibition. This information was used for synthesizing peptides, which were evaluated for inhibition against HMGCR. The stronger docking interactions were confirmed by experimental dissociation constant (K_D) values of 9.1 × 10^-9 M, 1.4 × 10^-8 M and 1.2 × 10^-8 M for peptides NALEPDNRIESEGG (Pep-1), NALEPDNRIES (Pep-2) and PFVKSEPIPETNNE (Pep-3) respectively. The immunological based interactions show a strong evidence of peptide-HMGCR complexes. The LDL uptake showed enhancements after treatments with peptides in the extracellular environment of HepG2 cells, which was further, corroborated through increase in the immunofluorescence signal of the localized LDL-R protein expression on the cell membrane. The results showed that the mRNA and protein expression of transcription factors were significantly up-regulated showing regulation of cholesterol biosynthesis in peptide treated HepG2 cells. The binding of transcription factors, sterol regulatory element (SRE) and cAMP-response element (CRE) on HMGCR promotor further confirms the cholesterol biosynthesis regulation. All the above results suggested a key role of peptide/s in alleviating cholesterol accumulation in tissue via inhibition of rate-limiting HMGCR enzyme.

Peptide and Protein Structure Prediction with a Simplified Continuum Solvent Model

A continuum solvent model based on screened Coulomb potentials has been simplified and parametrized to sample native-like structures in replica-exchange simulations of each of six different peptides and miniproteins. Low-energy, native, and non-native structures were used to iteratively refine 11 parameter values. The centroid of the largest cluster of structures sampled in simulations initiated from an extended conformation represents the predicted structure. The main-chain rms deviation of this prediction from the experimental structure was 0.47 Å for the 12-residue Trp-zip2, 0.86 Å for the 14-residue MBH12, 2.53 Å for the 17-residue U(1-17)T9D, 2.03 Å for the 20-residue BS1, 1.08 Å for the 20-residue Trp-cage, and 3.64 Å for the 35-residue villin headpiece subdomain HP35. The centroid of the sixth largest cluster sampled for HP35 deviated by 0.91 Å. The CHARMM22/CMAP force field was used, with an additional ψ torsion term for residues other than glycine and proline. Six parameters govern the dielectric response of the continuum solvent, and four values of surface tension approximate nonpolar effects. An atom's self-energy and interaction energies are screened independently, each depending on whether the atom is part of a charged group, a neutral hydrogen-bonding main-chain group, or any other neutral group. The parameters inferred result in strong main-chain hydrogen bonds, consistent with the view that protein folding is dominated by the formation of these bonds. (1,2) Conformations of MBH12 and BS1 were excluded from the energy-function refinement, suggesting the parameters, referred to as SCP18, are transferable. An efficient estimate of solvent-accessible surface area is also described.

Compound Molecular Logic in Accessing the Active Site of Mycobacterium tuberculosis Protein Tyrosine Phosphatase B

Protein tyrosine phosphatase B (PtpB) from Mycobacterium tuberculosis (Mtb) extends the bacteria's survival in hosts and hence is a potential target for Mtb-specific drugs. To study how Mtb-specific sequence insertions in PtpB may regulate access to its active site through large-amplitude conformational changes, we performed free-energy calculations using an all-atom explicit solvent model. Corroborated by biochemical assays, the results show that PtpB's active site is controlled via an "either/or" compound conformational gating mechanism, an unexpected discovery that Mtb has evolved to bestow a single enzyme with such intricate logical operations. In addition to providing unprecedented insights for its active-site surroundings, the findings also suggest new ways of inactivating PtpB.

Targeting the Pentose Phosphate Pathway: Characterization of a New 6PGL Inhibitor

Human African trypanosomiasis, or sleeping sickness, is a lethal disease caused by the protozoan parasite Trypanosoma brucei. However, although many efforts have been made to understand the biochemistry of this parasite, drug development has led to treatments that are of limited efficiency and of great toxicity. To develop new drugs, new targets must be identified, and among the several metabolic processes of trypanosomes that have been proposed as drug targets, carbohydrate metabolism (glycolysis and the pentose phosphate pathway (PPP)) appears as a promising one. As far as the PPP is concerned, a limited number of studies are related to the glucose-6-phosphate dehydrogenase. In this work, we have focused on the activity of the second PPP enzyme (6-phospho-gluconolactonase (6PGL)) that transforms 6-phosphogluconolactone into 6-phosphogluconic acid. A lactam analog of the natural substrate has been synthesized, and binding of the ligand to 6PGL has been investigated by NMR titration. The ability of this ligand to inhibit 6PGL has also been demonstrated using ultraviolet experiments, and protein-inhibitor interactions have been investigated through docking calculations and molecular dynamics simulations. In addition, a marginal inhibition of the third enzyme of the PPP (6-phosphogluconate dehydrogenase) was also demonstrated. Our results thus open new prospects for targeting T. brucei.

Explicit ions/implicit water generalized Born model for nucleic acids

The ion atmosphere around highly charged nucleic acid molecules plays a significant role in their dynamics, structure, and interactions. Here we utilized the implicit solvent framework to develop a model for the explicit treatment of ions interacting with nucleic acid molecules. The proposed explicit ions/implicit water model is based on a significantly modified generalized Born (GB) model and utilizes a non-standard approach to define the solute/solvent dielectric boundary. Specifically, the model includes modifications to the GB interaction terms for the case of multiple interacting solutes-disconnected dielectric boundary around the solute-ion or ion-ion pairs. A fully analytical description of all energy components for charge-charge interactions is provided. The effectiveness of the approach is demonstrated by calculating the potential of mean force for Na⁺-Cl^- ion pair and by carrying out a set of Monte Carlo (MC) simulations of mono- and trivalent ions interacting with DNA and RNA duplexes. The monovalent (Na⁺) and trivalent (CoHex³⁺) counterion distributions predicted by the model are in close quantitative agreement with all-atom explicit water molecular dynamics simulations used as reference. Expressed in the units of energy, the maximum deviations of local ion concentrations from the reference are within k _B T. The proposed explicit ions/implicit water GB model is able to resolve subtle features and differences of CoHex distributions around DNA and RNA duplexes. These features include preferential CoHex binding inside the major groove of the RNA duplex, in contrast to CoHex biding at the "external" surface of the sugar-phosphate backbone of the DNA duplex; these differences in the counterion binding patters were earlier shown to be responsible for the observed drastic differences in condensation propensities between short DNA and RNA duplexes. MC simulations of CoHex ions interacting with the homopolymeric poly(dA·dT) DNA duplex with modified (de-methylated) and native thymine bases are used to explore the physics behind CoHex-thymine interactions. The simulations suggest that the ion desolvation penalty due to proximity to the low dielectric volume of the methyl group can contribute significantly to CoHex-thymine interactions. Compared to the steric repulsion between the ion and the methyl group, the desolvation penalty interaction has a longer range and may be important to consider in the context of methylation effects on DNA condensation.

Helical Polyampholyte Sequences Have Unique Thermodynamic Properties

Helices are the most common structural pattern observed in structured proteins. Polypeptide sequences that form helices in isolation have been identified and extensively studied. These are generally rich in alanine, the amino acid with strongest helical propensity. Insertion of charged or polar amino acids has been shown to be necessary to make alanine-rich peptides soluble and sometimes even increase the helicity of the peptides. More recently sequences that contain mostly charged residues (E-R/K rich) have been found in naturally occurring proteins that are highly helical, soluble, and extended regardless their length. Artificial sequences composed mostly or exclusively of charged amino acids have been designed that are also highly helical, depending on the specific pattern of oppositely charged residues. Here we explore the thermodynamic properties of a number of 16-residue long peptides with varying helical propensity by performing equilibrium simulations over a broad range of temperatures. We observe quantitative differences in the peptides' helical propensities that can be related to qualitative differences in the free energy landscape, depending on the ampholytic patterns in the sequence. The results provide hints on how the specific physical properties of naturally occurring long sequences with similar patterns of charged residues may relate to their biological function.

Structural Analysis of Variability and Interaction of the N-terminal of the Oncogenic Effector CagA of Helicobacter pylori with Phosphatidylserine

Helicobacter pylori cytotoxin-associated gene A protein (CagA) has been associated with the increase in virulence and risk of cancer. It has been demonstrated that CagA’s translocation is dependent on its interaction with phosphatidylserine. We evaluated the variability of the N-terminal CagA in 127 sequences reported in NCBI, by referring to molecular interaction forces with the phosphatidylserine and the docking of three mutations chosen from variations in specific positions. The major sites of conservation of the residues involved in CagA–Phosphatidylserine interaction were 617, 621 and 626 which had no amino acid variation. Position 636 had the lowest conservation score; mutations in this position were evaluated to observe the differences in intermolecular forces for the CagA–Phosphatidylserine complex. We evaluated the docking of three mutations: K636A, K636R and K636N. The crystal and mutation models presented a ΔG of −8.919907, −8.665261, −8.701923, −8.515097 Kcal/mol, respectively, while mutations K636A, K636R, K636N and the crystal structure presented 0, 3, 4 and 1 H-bonds, respectively. Likewise, the bulk effect of the ΔG and amount of H-bonds was estimated in all of the docking models. The type of mutation affected both the ΔG (χ2(1)=93.82, p-value <2.2×10−16) and the H-bonds (χ2(1)=91.93, p-value <2.2×10−16). Overall, 76.9% of the strains that exhibit the K636N mutation produced a severe pathology. The average H-bond count diminished when comparing the mutations with the crystal structure of all the docking models, which means that other molecular forces are involved in the CagA–Phosphatidylserine complex interaction.

The Zinc Linchpin Motif in the DNA Repair Glycosylase MUTYH: Identifying the Zn2+ Ligands and Roles in Damage Recognition and Repair

The DNA base excision repair (BER) glycosylase MUTYH prevents DNA mutations by catalyzing adenine (A) excision from inappropriately formed 8-oxoguanine (8-oxoG):A mismatches. The importance of this mutation suppression activity in tumor suppressor genes is underscored by the association of inherited variants of MUTYH with colorectal polyposis in a hereditary colorectal cancer syndrome known as MUTYH-associated polyposis, or MAP. Many of the MAP variants encompass amino acid changes that occur at positions surrounding the two-metal cofactor-binding sites of MUTYH. One of these cofactors, found in nearly all MUTYH orthologs, is a [4Fe-4S]²⁺ cluster coordinated by four Cys residues located in the N-terminal catalytic domain. We recently uncovered a second functionally relevant metal cofactor site present only in higher eukaryotic MUTYH orthologs: a Zn²⁺ ion coordinated by three Cys residues located within the extended interdomain connector (IDC) region of MUTYH that connects the N-terminal adenine excision and C-terminal 8-oxoG recognition domains. In this work, we identified a candidate for the fourth Zn²⁺ coordinating ligand using a combination of bioinformatics and computational modeling. In addition, using in vitro enzyme activity assays, fluorescence polarization DNA binding assays, circular dichroism spectroscopy, and cell-based rifampicin resistance assays, the functional impact of reduced Zn²⁺ chelation was evaluated. Taken together, these results illustrate the critical role that the "Zn²⁺ linchpin motif" plays in MUTYH repair activity by providing for proper engagement of the functional domains on the 8-oxoG:A mismatch required for base excision catalysis. The functional importance of the Zn²⁺ linchpin also suggests that adjacent MAP variants or exposure to environmental chemicals may compromise Zn²⁺ coordination, and ability of MUTYH to prevent disease.

Protein Conformational Transitions from All-Atom Adaptively Biased Path Optimization

Simulation methods are valuable for elucidating protein conformational transitions between functionally diverse states given that transition pathways are difficult to capture experimentally. Nonetheless, specific computational algorithms are required because of the high free energy barriers between these different protein conformational states. Adaptively biased path optimization (ABPO) is an unrestrained, transition-path optimization method that works in a reduced-variable space to construct an adaptive biasing potential to aid convergence. ABPO was previously applied using a coarse-grained Go̅-model to study conformational activation of Lyn, a Src family tyrosine kinase. How effectively ABPO can be applied at the higher resolution of an all-atom model to explore protein conformational transitions is not yet known. Here, we report the all-atom conformational transition paths of three protein systems constructed using the ABPO methodology. Two systems, triose phosphate isomerase and dihydrofolate reductase, undergo local flipping of a short loop that promotes ligand binding. The third system, estrogen receptor α ligand binding domain, has a helix that adopts different conformations when the protein is bound to an agonist or an antagonist. For each protein, distance-based or torsion-angle reduced variables were identified from unbiased trajectories. ABPO was computed in this reduced variable space to obtain the transition path between the two states. The all-atom ABPO is shown to successfully converge an optimal transition path for each of the three systems.

Elucidating the multi-targeted anti-amyloid activity and enhanced islet amyloid polypeptide binding of β-wrapins

β-wrapins are engineered binding proteins stabilizing the β-hairpin conformations of amyloidogenic proteins islet amyloid polypeptide (IAPP), amyloid-β, and α-synuclein, thus inhibiting their amyloid propensity. Here, we use computational and experimental methods to investigate the molecular recognition of IAPP by β-wrapins. We show that the multi-targeted, IAPP, amyloid-β, and α-synuclein, binding properties of β-wrapins originate mainly from optimized interactions between β-wrapin residues and sets of residues in the three amyloidogenic proteins with similar physicochemical properties. Our results suggest that IAPP is a comparatively promiscuous β-wrapin target, probably due to the low number of charged residues in the IAPP β-hairpin motif. The sub-micromolar affinity of β-wrapin HI18, specifically selected against IAPP, is achieved in part by salt-bridge formation between HI18 residue Glu10 and the IAPP N-terminal residue Lys1, both located in the flexible N-termini of the interacting proteins. Our findings provide insights towards developing novel protein-based single- or multi-targeted therapeutics.