Influence of Association on Binding of Disaccharides to YKL-39 and hHyal-1 Enzymes

Disaccharide complexes have been shown experimentally to be useful for drug delivery or as an antifouling surface biofilm, and are promising drug-encapsulation and delivery candidates. Although such complexes are intended for medical applications, to date no studies at the molecular level have been devoted to the influence of complexation on the enzymatic decomposition of polysaccharides. A theoretical approach to this problem has been hampered by the lack of a suitable computational tool for binding such non-covalent complexes to enzymes. Herein, we combine quantum-mechanical calculations of disaccharides complexes with a nonstandard docking GaudiMM engine that can perform such a task. Our results on four different complexes show that they are mostly stabilized by electrostatic interactions and hydrogen bonds. This strong non-covalent stabilization demonstrates the studied complexes are some excellent candidates for self-assembly smart materials, useful for drug encapsulation and delivery. Their advantage lies also in their biocompatible and biodegradable character.     

Enzyme-Specific Activation versus Leaving Group Ability

Enzyme-specific activation and the substrate mimetics strategy are effective ways to circumvent the limited substrate recognition often encountered in protease-catalyzed peptide synthesis. A key structural element in both approaches is the guanidinophenyl (OGp) ester, which enables important interactions for affinity and recognition by the enzyme-at least, this is usually the explanation given for its successful application. In this study we show that leaving group ability is of equal or even greater importance. To this end we used both experimental and computational methods: 1) synthesis of close analogues of OGp, and their evaluation in a dipeptide synthesis assay with trypsin, 2) molecular docking studies to provide insights into the binding mode, and 3) ab initio calculations to evaluate their electronic properties.     

Understanding the Selectivity Mechanism of the Human Asialoglycoprotein Receptor (ASGP-R) toward Gal- and Man-type Ligands for Predicting Interactions with Exogenous Sugars

A practical approach for addressing the computer simulation of protein-carbohydrate interactions is described here. An articulated computational protocol was set up and validated by checking its ability to predict experimental data, available in the literature, and concerning the selectivity shown by the Carbohydrate Recognition Domain (CRD) of the human asialoglycoprotein receptor (ASGP-R) toward Gal-type ligands. Some required features responsible for the interactions were identified. Subsequently the same protocol was applied to monomer sugar molecules that constitute the building blocks for alginates and ulvans. Such sugar polymers may supply a low-cost source of rare sugars with a potential impact on several industrial applications, from pharmaceutical to fine chemical industry. An example of their applicative exploitation could be given by their use in developing biomaterial with adhesion properties toward hepatocytes, through interaction with the ASGP-R. Such a receptor has been already proposed as a target for exogenous molecules, specifically in the case of hepatocytes, for diagnostic and therapeutic purposes. The DOCK5.2 program was used to search optimal locations of the above ligands of interest into CRD binding site and to roughly estimate interaction energies. Finally, the binding ΔG of theoretical protein-ligand complexes was estimated by using the DelPhi program in which the solvation free energy is accounted for with a continuum solvent model, by solving the Poisson-Boltzmann equation. The structure analysis of the obtained complexes and their ΔG values suggest that one of the sugar monomers of interest shows the desired characteristics.     

Methods and Applications of In Silico Aptamer Design and Modeling

Aptamers are nucleic acid analogues of antibodies with high affinity to different targets, such as cells, viruses, proteins, inorganic materials, and coenzymes. Empirical approaches allow the design of in vitro aptamers that bind particularly to a target molecule with high affinity and selectivity. Theoretical methods allow significant expansion of the possibilities of aptamer design. In this study, we review theoretical and joint theoretical-experimental studies dedicated to aptamer design and modeling. We consider aptamers with different targets, such as proteins, antibiotics, organophosphates, nucleobases, amino acids, and drugs. During nucleic acid modeling and in silico design, a full set of in silico methods can be applied, such as docking, molecular dynamics (MD), and statistical analysis. The typical modeling workflow starts with structure prediction. Then, docking of target and aptamer is performed. Next, MD simulations are performed, which allows for an evaluation of the stability of aptamer/ligand complexes and determination of the binding energies with higher accuracy. Then, aptamer/ligand interactions are analyzed, and mutations of studied aptamers made. Subsequently, the whole procedure of molecular modeling can be reiterated. Thus, the interactions between aptamers and their ligands are complex and difficult to understand using only experimental approaches. Docking and MD are irreplaceable when aptamers are studied in silico.     

Characterization of Aptamer-Protein Complexes by X-ray Crystallography and Alternative Approaches

Aptamers are oligonucleotide ligands, either RNA or ssDNA, selected for high-affinity binding to molecular targets, such as small organic molecules, proteins or whole microorganisms. While reports of new aptamers are numerous, characterization of their specific interaction is often restricted to the affinity of binding (K(D)). Over the years, crystal structures of aptamer-protein complexes have only scarcely become available. Here we describe some relevant technical issues about the process of crystallizing aptamer-protein complexes and highlight some biochemical details on the molecular basis of selected aptamer-protein interactions. In addition, alternative experimental and computational approaches are discussed to study aptamer-protein interactions.     

Multipose Binding in Molecular Docking

Molecular docking has been extensively applied in virtual screening of small molecule libraries for lead identification and optimization. A necessary prerequisite for successful differentiation between active and non-active ligands is the accurate prediction of their binding affinities in the complex by use of docking scoring functions. However, many studies have shown rather poor correlations between docking scores and experimental binding affinities. Our work aimed to improve this correlation by implementing a multipose binding concept in the docking scoring scheme. Multipose binding, i.e., the property of certain protein-ligand complexes to exhibit different ligand binding modes, has been shown to occur in nature for a variety of molecules. We conducted a high-throughput docking study and implemented multipose binding in the scoring procedure by considering multiple docking solutions in binding affinity prediction. In general, improvement of the agreement between docking scores and experimental data was observed, and this was most pronounced in complexes with large and flexible ligands and high binding affinities. Further developments of the selection criteria for docking solutions for each individual complex are still necessary for a general utilization of the multipose binding concept for accurate binding affinity prediction by molecular docking.     

Advances and challenges in protein-ligand docking

Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions-the three important aspects in protein-ligand docking. Challenges and possible future directions are discussed in the Conclusion.     

A Fragment-Based Scoring Function to Re-rank ATP Docking Results

ATP is involved in numerous biochemical reactions in living cells interacting with different proteins. Molecular docking simulations provide considerable insight into the problem of molecular recognition of this substrate. To improve the selection of correct ATP poses among those generated by docking algorithms we propose a post-docking reranking criterion. The method is based on detailed analysis of the intermolecular interactions in 50 high-resolution 3D-structures of ATP-protein complexes. A distinctive new feature of the proposed method is that the ligand molecule is divided into fragments that differ in their physical properties. The placement of each of them into the binding site is judged separately by different criteria, thus avoiding undesirable averaging of the scoring function terms by highlighting those relevant for particular fragments. The scoring performance of the new criteria was tested with the docking solutions for ATPprotein complexes and a significant improvement in the selection of correct docking poses was observed, as compared to the standard scoring function.     

Protein-Protein Interaction Modeling and Inhibition: The TAU Bioinfo3D Perspective

Protein-protein interactions are central to cell function. In order to fully understand these interactions, one has to elucidate the three-dimensional structures of the underlying complexes. While experimental methods have advanced significantly in the last decade, there are still few structures of protein complexes in the Protein Data Bank. Reliable computational techniques are required to fill in this gap. Better understanding of protein-protein interactions has also opened a large number of potential targets for the pharmaceutical industry, which previously viewed these interactions as “undruggable”. In this review, we focus on the algorithms developed by the Tel Aviv University Structural Bioinformatics (Bioinfo3D) Lab to model protein-protein interactions, and on a preliminary attempt to search for peptide inhibitors for these interactions. All the algorithms presented are among the fastest available today and can be accessed via the group web server.     

Combining Molecular Docking and Molecular Dynamics to Predict the Binding Modes of Flavonoid Derivatives with the Neuraminidase of the 2009 H1N1 Influenza A Virus

Control of flavonoid derivatives inhibitors release through the inhibition of neuraminidase has been identified as a potential target for the treatment of H1N1 influenza disease. We have employed molecular dynamics simulation techniques to optimize the 2009 H1N1 influenza neuraminidase X-ray crystal structure. Molecular docking of the compounds revealed the possible binding mode. Our molecular dynamics simulations combined with the solvated interaction energies technique was applied to predict the docking models of the inhibitors in the binding pocket of the H1N1 influenza neuraminidase. In the simulations, the correlation of the predicted and experimental binding free energies of all 20 flavonoid derivatives inhibitors is satisfactory, as indicated by R(2) = 0.75.     

Docking of cytochrome c6 and plastocyanin to the aa3-type cytochrome c oxidase in the cyanobacterium Phormidium laminosum

The interactions between redox proteins are transient in nature. Therefore, very few crystal structures are available for the complexes formed between these proteins. Computational docking simulations thus provide a useful alternative method for studying the interactions between electron transfer proteins. In this paper, we have studied the interactions between the aa(3)-type cytochrome c oxidase of the cyanobacterium Phormidium laminosum and its redox partners plastocyanin and cytochrome c(6) using a combination of comparative modelling techniques and docking simulations. Rigid-body docking orientations were scored with a combined energy function that accounts for electrostatics and desolvation. These simulations have identified two plausible docking sites, one of which appears to be unique to the binding of plastocyanin to the oxidase. This unique binding site may be due to the presence of a long loop region in the subunit II of cyanobacterial oxidases. Control simulations were performed with the ba(3)-type cytochrome c oxidase and its redox partner cytochrome c(552) from Thermus thermophilus. The docking between cytochrome c oxidase and its redox partners plastocyanin and cytochrome c(6) is dominated by hydrophobic residues, a feature already observed from kinetic and structural studies in other complexes of P. laminosum (e.g. plastocyanin or cytochrome c(6) with cytochrome f and photosystem I).     

Systematic placement of structural water molecules for improved scoring of protein-ligand interactions

Structural water molecules are found in many protein-ligand complexes. They are known to be vital in mediating hydrogen-bonding interactions and, in some cases, key for facilitating tight binding. It is thus very important to consider water molecules when attempting to model protein-ligand interactions for cognate ligand identification, virtual screening and drug design. While the rigid treatment of water molecules present in structures is feasible, the more relevant task of treating all possible positions and orientations of water molecules with each possible ligand pose is computationally daunting. Current methods in molecular docking provide partial treatment for such water molecules, with modest success. Here we describe a new method employing dead-end elimination to place water molecules within a binding site, bridging interactions between protein and ligand. Dead-end elimination permits a thorough, though still incomplete, treatment of water placement. The results show that this method is able to place water molecules correctly within known complexes and to create physically reasonable hydrogen bonds. The approach has also been incorporated within an inverse molecular design approach, to model a variety of compounds in the process of de novo ligand design. The inclusion of structural water molecules, combined with ranking based on the electrostatic contribution to binding affinity, improves a number of otherwise poor energetic predictions.     

A Library of Thiazolidin-4-one Derivatives as Protein Tyrosine Phosphatase 1B (PTP1B) Inhibitors: An Attempt To Discover Novel Antidiabetic Agents

Protein tyrosine phosphatase 1B (PTP1B) is an important target for the treatment of diabetes. A series of thiazolidin-4-one derivatives 8 – 22 was designed, synthesized and investigated as PTP1B inhibitors. The new molecules inhibited PTP1B with IC₅₀ values in the micromolar range. 5-(Furan-2-ylmethylene)-2-(4-nitrophenylimino)thiazolidin-4-one ( 17 ) exhibited potency with a competitive type of enzyme inhibition. structure–activity relationship studies revealed various structural facets important for the potency of these analogues. The findings revealed a requirement for a nitro group-including hydrophobic heteroaryl ring for PTP1B inhibition. Molecular docking studies afforded good correlation with experimental results. H-bonding and π–π interactions were responsible for optimal binding and effective stabilization of virtual protein-ligand complexes. Furthermore, in-silico pharmacokinetic properties of test compounds predicted their drug-like characteristics for potential oral use as antidiabetic agents.Additionally, a binding site model demonstrating crucial pharmacophoric characteristics influencing potency and binding affinity of inhibitors has been proposed, which can be employed in the design of future potential PTP1B inhibitors.     

Virtual Screening of Specific Insulin-Like Growth Factor 1 Receptor (IGF1R) Inhibitors from the National Cancer Institute (NCI) Molecular Database

Insulin-like growth factor 1 receptor (IGF1R) is an attractive drug target for cancer therapy and research on IGF1R inhibitors has had success in clinical trials. A particular challenge in the development of specific IGF1R inhibitors is interference from insulin receptor (IR), which has a nearly identical sequence. A few potent inhibitors that are selective for IGF1R have been discovered experimentally with the aid of computational methods. However, studies on the rapid identification of IGF1R-selective inhibitors using virtual screening and confidence-level inspections of ligands that show different interactions with IGF1R and IR in docking analysis are rare. In this study, we established virtual screening and binding-mode prediction workflows based on benchmark results of IGF1R and several kinase receptors with IGF1R-like structures. We used comprehensive analysis of the known complexes of IGF1R and IR with their binding ligands to screen specific IGF1R inhibitors. Using these workflows, 17 of 139,735 compounds in the NCI (National Cancer Institute) database were identified as potential specific inhibitors of IGF1R. Calculations of the potential of mean force (PMF) with GROMACS were further conducted for three of the identified compounds to assess their binding affinity differences towards IGF1R and IR.     

Prediction of small-molecule binding to cytochrome P450 3A4: flexible docking combined with multidimensional QSAR

The inhibition of cytochrome P450 3A4 (CYP3A4) by small molecules is a major mechanism associated with undesired drug-drug interactions, which are responsible for a substantial number of late-stage failures in the pharmaceutical drug-development process. For a quantitative prediction of associated pharmacokinetic parameters, a computational model was developed that allows prediction of the inhibitory potential of 48 structurally diverse molecules. Based on the experimental structure of CYP3A4, possible binding modes were first sampled by using automated docking (Yeti software) taking protein flexibility into account. The results are consistent with both X-ray crystallographic data and data from metabolic studies. Next, an ensemble of energetically favorable orientations was composed into a 4D dataset for use as input for a multidimensional QSAR technique (Raptor software). A dual-shell binding-site model that allows an explicit induced fit was then generated by using hydrophobicity scoring and hydrogen-bond propensity. The simulation reached a cross-validated r2 value of 0.825 and a predictive r2 value of 0.659. On average, the predicted binding affinity of the training ligands deviates by a factor of 2.7 from the experiment; those of the test set deviate by a factor of 3.8 in Ki.     

Design,Synthesis, in vitro MAO‐B Inhibitory Evaluation,and Computational Studies of Some 6‐Nitrobenzothiazole‐Derived Semicarbazones

Monoamine oxidase B (MAO‐B) is an important drug target for the treatment of neurological disorders. A series of 6‐nitrobenzothiazole‐derived semicarbazones were designed, synthesized, and evaluated as inhibitors of the rat brain MAO‐B isoenzyme. Most of the compounds were found to be potent inhibitors of MAO‐B, with IC₅₀ values in the nanomolar to micromolar range. Molecular docking studies were performed with AutoDock 4.2 to deduce the affinity and binding mode of these inhibitors toward the MAO‐B active site. The free energies of binding (ΔG) and inhibition constants (K_i) of the docked compounds were calculated by the Lamarckian genetic algorithm (LGA) of AutoDock 4.2. Good correlations between the calculated and experimental results were obtained. 1‐[(4‐Chlorophenyl)(phenyl)methylene]‐4‐(6‐nitrobenzothiazol‐2‐yl)semicarbazide emerged as the lead MAO‐B inhibitor, with top ranking in both the experimental MAO‐B assay (IC₅₀: 0.004±0.001 μM ) and in computational docking studies (K_i: 1.08 μM ). Binding mode analysis of potent inhibitors suggests that these compounds are well accommodated by the MAO‐B active site through stable hydrophobic and hydrogen bonding interactions. Interestingly, the 6‐nitrobenzothiazole moiety is stabilized in the substrate cavity with the aryl or diaryl residues extending up into the entrance cavity of the active site. According to our results, docking experiments could be an interesting approach for predicting the activity and binding interactions of this class of semicarbazones against MAO‐B. Thus, a binding site model consisting of three essential pharmacophoric features is proposed, and this can be used for the design of future MAO‐B inhibitors.     

Structural and Computational Studies of the SARS-CoV-2 Spike Protein Binding Mechanisms with Nanobodies: From Structure and Dynamics to Avidity-Driven Nanobody Engineering

Nanobodies provide important advantages over traditional antibodies, including their smaller size and robust biochemical properties such as high thermal stability, high solubility, and the ability to be bioengineered into novel multivalent, multi-specific, and high-affinity molecules, making them a class of emerging powerful therapies against SARS-CoV-2. Recent research efforts on the design, protein engineering, and structure-functional characterization of nanobodies and their binding with SARS-CoV-2 S proteins reflected a growing realization that nanobody combinations can exploit distinct binding epitopes and leverage the intrinsic plasticity of the conformational landscape for the SARS-CoV-2 S protein to produce efficient neutralizing and mutation resistant characteristics. Structural and computational studies have also been instrumental in quantifying the structure, dynamics, and energetics of the SARS-CoV-2 spike protein binding with nanobodies. In this review, a comprehensive analysis of the current structural, biophysical, and computational biology investigations of SARS-CoV-2 S proteins and their complexes with distinct classes of nanobodies targeting different binding sites is presented. The analysis of computational studies is supplemented by an in-depth examination of mutational scanning simulations and identification of binding energy hotspots for distinct nanobody classes. The review is focused on the analysis of mechanisms underlying synergistic binding of multivalent nanobodies that can be superior to single nanobodies and conventional nanobody cocktails in combating escape mutations by effectively leveraging binding avidity and allosteric cooperativity. We discuss how structural insights and protein engineering approaches together with computational biology tools can aid in the rational design of synergistic combinations that exhibit superior binding and neutralization characteristics owing to avidity-mediated mechanisms.     

Identification of Novel Potential β-N-Acetyl-D-Hexosaminidase Inhibitors by Virtual Screening, Molecular Dynamics Simulation and MM-PBSA Calculations

Chitinolytic β-N-acetyl-d-hexosaminidases, as a class of chitin hydrolysis enzyme in insects, are a potential species-specific target for developing environmentally-friendly pesticides. Until now, pesticides targeting chitinolytic β-N-acetyl-d-hexosaminidase have not been developed. This study demonstrates a combination of different theoretical methods for investigating the key structural features of this enzyme responsible for pesticide inhibition, thus allowing for the discovery of novel small molecule inhibitors. Firstly, based on the currently reported crystal structure of this protein (OfHex1.pdb), we conducted a pre-screening of a drug-like compound database with 8 × 10(6) compounds by using the expanded pesticide-likeness criteria, followed by docking-based screening, obtaining 5 top-ranked compounds with favorable docking conformation into OfHex1. Secondly, molecular docking and molecular dynamics simulations are performed for the five complexes and demonstrate that one main hydrophobic pocket formed by residues Trp424, Trp448 and Trp524, which is significant for stabilization of the ligand-receptor complex, and key residues Asp477 and Trp490, are respectively responsible for forming hydrogen-bonding and π-π stacking interactions with the ligands. Finally, the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) analysis indicates that van der Waals interactions are the main driving force for the inhibitor binding that agrees with the fact that the binding pocket of OfHex1 is mainly composed of hydrophobic residues. These results suggest that screening the ZINC database can maximize the identification of potential OfHex1 inhibitors and the computational protocol will be valuable for screening potential inhibitors of the binding mode, which is useful for the future rational design of novel, potent OfHex1-specific pesticides.     

The 2-hydroxy-3-(4-aryl-1-piperazinyl)propyl Phthalimide Derivatives as Prodrugs—Spectroscopic and Theoretical Binding Studies with Plasma Proteins

Many publications in databases deal with the interactions of new drugs with albumin. However, it is not only albumin that is responsible for binding pharmaceutical molecules to proteins in the human body. There are many more proteins in plasma that are important for the study of the ADME pathway. Therefore, in this study, we have shown the results of the interactions between the plasma proteins albumin, orosomucoid, and gamma globulins and non-toxic anti-inflammatory phthalimide analogs, which due to the promising obtained results, may be potential candidates in the group of analgesic and anti-inflammatory drugs. Using spectroscopic methods and molecular modeling, we showed that all four tested compounds form complexes with the analyzed proteins. The formation of a complex with proteins raises the pharmacological efficacy of the drug. Therefore, the obtained results could be a step in the study of the pharmacokinetics and pharmacodynamics of new potential pharmaceuticals.