The structure of a novel psychrophilic β-mannanase enzyme from Glaciozyma antarctica PI12 yeast has been modelled and analysed in detail. To our knowledge, this is the first attempt to model a psychrophilic β-mannanase from yeast. To this end, a 3D structure of the enzyme was first predicted using a threading method because of the low sequence identity (<30%) using MODELLER9v12 and simulated using GROMACS at varying low temperatures for structure refinement. Comparisons with mesophilic and thermophilic mannanases revealed that the psychrophilic mannanase contains longer loops and shorter helices, increases in the number of aromatic and hydrophobic residues, reductions in the number of hydrogen bonds and salt bridges and numerous amino acid substitutions on the surface that increased the flexibility and its efficiency for catalytic reactions at low temperatures.
The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.
Phenolic Schiff bases are known for their diverse biological activities and ability to scavenge free radicals. To elucidate (1) the structure-antioxidant activity relationship of a series of thirty synthetic derivatives of 2-methoxybezohydrazide phenolic Schiff bases and (2) to determine the major mechanism involved in free radical scavenging, we used density functional theory calculations (B3P86/6-31+(d,p)) within polarizable continuum model. The results showed the importance of the bond dissociation enthalpies (BDEs) related to the first and second (BDEd) hydrogen atom transfer (intrinsic parameters) for rationalizing the antioxidant activity. In addition to the number of OH groups, the presence of a bromine substituent plays an interesting role in modulating the antioxidant activity. Theoretical thermodynamic and kinetic studies demonstrated that the free radical scavenging by these Schiff bases mainly proceeds through proton-coupled electron transfer rather than sequential proton loss electron transfer, the latter mechanism being only feasible at relatively high pH.
The structure of psychrophilic chitinase (CHI II) from Glaciozyma antarctica PI12 has yet to be studied in detail. Due to its low sequence identity (<30 %), the structural prediction of CHI II is a challenge. A 3D model of CHI II was built by first using a threading approach to search for a suitable template and to generate an optimum target-template alignment, followed by model building using MODELLER9v7. Analysis of the catalytic insertion domain structure in CHI II revealed an increase in the number of aromatic residues and longer loops compared to mesophilic and thermophilic chitinases. A molecular dynamics simulation was used to examine the stability of the CHI II structure at 273, 288 and 300 K. Structural analysis of the substrate-binding cleft revealed a few exposed aromatic residues. Substitutions of certain amino acids in the surface and loop regions of CHI II conferred an increased flexibility to the enzyme, allowing for an adaptation to cold temperatures. A substrate binding comparison of CHI II with the mesophilic chitinase from Coccidioides immitis, 1D2K, suggested that the psychrophilic adaptation and catalytic activity at low temperatures were achieved through a reduction in the number of salt bridges, fewer hydrogen bonds and an increase in the exposure of the hydrophobic side chains to the solvent.
Query expansion is the process of reformulating an original query to improve retrieval performance in information retrieval systems. Relevance feedback is one of the most useful query modification techniques in information retrieval systems. In this paper, we introduce query expansion into ligand-based virtual screening (LBVS) using the relevance feedback technique. In this approach, a few high-ranking molecules of unknown activity are filtered from the outputs of a Bayesian inference network based on a single ligand molecule to form a set of ligand molecules. This set of ligand molecules is used to form a new ligand molecule. Simulated virtual screening experiments with the MDL Drug Data Report and maximum unbiased validation data sets show that the use of ligand expansion provides a very simple way of improving the LBVS, especially when the active molecules being sought have a high degree of structural heterogeneity. However, the effectiveness of the ligand expansion is slightly less when structurally-homogeneous sets of actives are being sought.
Mycobacterium tuberculosis (Mtb) 16.3 kDa heat shock protein 16.3 (HSP16.3) is a latency-associated antigen that can be targeted for latent tuberculosis (TB) diagnostic and therapeutic development. We have previously developed human VH domain antibodies (dAbs; clone E3 and F1) specific against HSP16.3. In this work, we applied computational methods to optimise and design the antibodies in order to improve the binding affinity with HSP16.3. The VH domain antibodies were first docked to the dimer form of HSP16.3 and further sampled using molecular dynamics simulation. The calculated binding free energy of the HSP16.3-dAb complexes showed non-polar interactions were responsible for the antigen-antibody association. Per-residue free energy decomposition and computational alanine scanning have identified one hotspot residue for E3 (Y391) and 4 hotspot residues for F1 (M394, Y396, R397 and M398). These hotspot residues were then mutated and evaluated by binding free energy calculations. Phage ELISA assay was carried out on the potential mutants (E3Y391W, F1M394E, F1R397N and F1M398Y). The experimental assay showed improved binding affinities of E3Y391W and F1M394E against HSP16.3 compared with the wild type E3 and F1. This case study has thus showed in silico methods are able to assist in optimisation or improvement of antibody-antigen binding.
The recent expansion of GPCR crystal structures provides the opportunity to assess the performance of structure-based drug design methods for the GPCR superfamily. Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA)-based methods are commonly used for binding affinity prediction, as they provide an intermediate compromise of speed and accuracy between the empirical scoring functions used in docking and more robust free energy perturbation methods. In this study, we systematically assessed the performance of MM/PBSA in predicting experimental binding free energies using twenty Class A GPCR crystal structures and 934 known ligands. Correlations between predicted and experimental binding free energies varied significantly between individual targets, ranging from r = - 0.334 in the inactive-state CB1 cannabinoid receptor to r = 0.781 in the active-state CB1 cannabinoid receptor, while average correlation across all twenty targets was relatively poor (r = 0.183). MM/PBSA provided better predictions of binding free energies compared to docking scores in eight out of the twenty GPCR targets while performing worse for four targets. MM/PBSA binding affinity predictions calculated using a single, energy minimized structure provided comparable predictions to sampling from molecular dynamics simulations and may be more efficient when computational cost becomes restrictive. Additionally, we observed that restricting MM/PBSA calculations to ligands with a high degree of structural similarity to the crystal structure ligands improved performance in several cases. In conclusion, while MM/PBSA remains a valuable tool for GPCR structure-based drug design, its performance in predicting the binding free energies of GPCR ligands remains highly system-specific as demonstrated in a subset of twenty Class A GPCRs, and validation of MM/PBSA-based methods for each individual case is recommended before prospective use.
Recent breakthroughs in G protein-coupled receptor (GPCR) crystallography and the subsequent increase in number of solved GPCR structures has allowed for the unprecedented opportunity to utilize their experimental structures for structure-based drug discovery applications. As virtual screening represents one of the primary computational methods used for the discovery of novel leads, the GPCR-Bench dataset was created to facilitate comparison among various virtual screening protocols. In this study, we have benchmarked the performance of Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) in improving virtual screening enrichment in comparison to docking with Glide, using the entire GPCR-Bench dataset of 24 GPCR targets and 254,646 actives and decoys. Reranking the top 10% of the docked dataset using MM/PBSA resulted in improvements for six targets at EF1% and nine targets at EF5%, with the gains in enrichment being more pronounced at the EF1% level. We additionally assessed the utility of rescoring the top ten poses from docking and the ability of short MD simulations to refine the binding poses prior to MM/PBSA calculations. There was no clear trend of the benefit observed in both cases, suggesting that utilizing a single energy minimized structure for MM/PBSA calculations may be the most computationally efficient approach in virtual screening. Overall, the performance of MM/PBSA rescoring in improving virtual screening enrichment obtained from docking of the GPCR-Bench dataset was found to be relatively modest and target-specific, highlighting the need for validation of MM/PBSA-based protocols prior to prospective use.
Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.
[Formula: see text]-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD <2.0 Å) and 78% of the cases are better predicted than the two other methods compared. Our method provides an alternative for modeling TM bitopic dimers of unknown structures for further computational studies. TMDIM is freely available on the web at https://cbbio.cis.umac.mo/TMDIM . Website is implemented in PHP, MySQL and Apache, with all major browsers supported.