Biomedical subjects

Anthony E Klon

Publications and source records attributed to Anthony E Klon.

9 recordsLinked to original sources

PubMed2004

Double belt structure of discoidal high density lipoproteins: molecular basis for size heterogeneity.

We recently proposed an all-atom model for apolipoprotein (apo) A-I in discoidal high-density lipoprotein in which two monomers form stacked antiparallel helical rings rotationally aligned by interhelical salt-bridges. The model can be derived a priori from the geometry of a planar bilayer disc that constrains the hydrophobic face of a continuous amphipathic alpha helix in lipid-associated apoA-I to a plane inside of an alpha-helical torus. This constrains each apoA-I monomer to a novel conformation, that of a slightly unwound, curved, planar amphipathic alpha 11/3 helix (three turns per 11 residues). Using non-denaturing gradient gel electrophoresis, we show that dimyristoylphosphocholine discs containing two apoA-I form five distinct particles with maximal Stokes diameters of 98 A (R2-1), 106 A (R2-2), 110 A (R2-3), 114 A (R2-4) and 120 A (R2-5). Further, we show that the Stokes diameters of R2-1 and R2-2 are independent of the N-terminal 43 residues (the flexible domain) of apoA-I, while the flexible domain is necessary and sufficient for the formation of the three larger complexes. On the basis of these results, the conformation of apoA-I on the R2-2 disc can be modeled accurately as an amphipathic helical double belt extending the full length of the lipid-associating domain with N and C-terminal ends in direct contact. The smallest of the discs, R2-1, models as the R2-2 conformation with an antiparallel 15-18 residue pairwise segment of helixes hinged off the disc edge. The conformations of full-length apoA-I on the flexible domain-dependent discs (R2-3, R2-4 and R2-5) model as the R2-2 conformation extended on the disc edge by one, two or three of the 11-residue tandem amphipathic helical repeats (termed G1, G2 and G3), respectively, contained within the flexible domain. Although we consider these results to favor the double belt model, the topographically very similar hairpin-belt model cannot be ruled out entirely.

Apolipoprotein A-I↗

PubMed2004

Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results.

We have previously shown that a machine learning technique can improve the enrichment of high-throughput docking (HTD) results. In the previous cases studied, however, the application of a naive Bayes classifier failed to improve enrichment for instances where HTD alone was unable to generate an acceptable enrichment. We present here a protocol to rescue poor docking results a priori using a combination of rank-by-median consensus scoring and naive Bayesian categorization.

Algorithms↗

PubMed2004

Finding more needles in the haystack: A simple and efficient method for improving high-throughput docking results.

The technology underpinning high-throughput docking (HTD) has developed over the past few years to where it has become a vital tool in modern drug discovery. Although the performance of various docking algorithms is adequate, the ability to accurately and consistently rank compounds using a scoring function remains problematic. We show that by employing a simple machine learning method (naïve Bayes) it is possible to significantly overcome this deficiency. Compounds from the Available Chemical Directory (ACD), along with known active compounds, were docked into two protein targets using three software packages. In cases where HTD alone was able to show some enrichment, the application of naïve Bayes was able to improve upon the enrichment. The application of this methodology to enrich HTD results can be carried out without a priori knowledge of the activity of compounds and results in superior enrichment of known actives compared to the use of scoring methods alone.

Bayes Theorem↗

PubMed2004

Enrichment of extremely noisy high-throughput screening data using a naïve Bayes classifier.

The noise level of a high-throughput screening (HTS) experiment depends on various factors such as the quality and robustness of the assay itself and the quality of the robotic platform. Screening of compound mixtures is noisier than screening single compounds per well. A classification model based on naïve Bayes (NB) may be used to enrich such data. The authors studied the ability of the NB classifier to prioritize noisy primary HTS data of compound mixtures (5 compounds/well) in 4 campaigns in which the percentage of noise presumed to be inactive compounds ranged between 81% and 91%. The top 10% of the compounds suggested by the classifier captured between 26% and 45% of the active compounds. These results are reasonable and useful, considering the poor quality of the training set and the short computing time that is needed to build and deploy the classifier.

Bayes Theorem↗

PubMed2002

Molecular dynamics simulations on discoidal HDL particles suggest a mechanism for rotation in the apo A-I belt model.

Apolipoprotein A-I (apo A-I) is the major protein component of high-density lipoprotein (HDL) particles. Elevated levels of HDL in the bloodstream have been shown to correlate strongly with a reduced risk factor for atherosclerosis. Molecular dynamics simulations have been carried out on three separate model discoidal high-density lipoprotein particles (HDL) containing two monomers of apo A-I and 160 molecules of palmitoyloleoylphosphatidylcholine (POPC), to a time-scale of 1ns. The starting structures were on the basis of previously published molecular belt models of HDL consisting of the lipid-binding C-terminal domain (residues 44-243) wrapped around the circumference of a discoidal HDL particle. Subtle changes between two of the starting structures resulted in significantly different behavior during the course of the simulation. The results provide support for the hypothesis of Segrest et al. that helical registration in the molecular belt model of apo A-I is modulated by intermolecular salt bridges. In addition, we propose an explanation for the presence of proline punctuation in the molecular belt model, and for the presence of two 11-mer helical repeats interrupting the otherwise regular pattern of 22-mer helical repeats in the lipid-binding domain of apo A-I.

1,2-Dipalmitoylphosphatidylcholine↗

PubMed2002

Comparative models for human apolipoprotein A-I bound to lipid in discoidal high-density lipoprotein particles.

We have constructed a series of models for apolipoprotein A-I (apo A-I) bound to discoidal high-density lipoprotein (HDL) particles, based upon the molecular belt model [Segrest, J. P., et al. (1999) J. Biol. Chem. 274, 31755-31758] and helical hairpin models [Rogers, D. P., et al. (1998) Biochemistry 37, 11714-11725], and compared these with picket fence models [Phillips, J. C., et al. (1997) Biophys. J. 73, 2337-2346]. Molecular belt models for discoidal HDL particles with differing diameters are presented, illustrating that the belt model can explain the discrete changes in HDL particle size observed experimentally. Hairpin models are discussed for the binding of apo A-I to discoidal HDL particles with diameters identical to those for the molecular belt model. Two models are presented for the binding of three monomers of apo A-I to a 150 A diameter discoidal HDL particle. In one model, two monomers of apo A-I bind to the exterior of the HDL particle in an antiparallel belt, with a third monomer of apo A-I bound to the disk in a hairpin conformation. In the second model, all three monomers of apo A-I are bound to the discoidal HDL particle in a hairpin conformation. Previously published experimental data for each model are reviewed, with FRET favoring either the belt or hairpin models over the picket fence models for HDL particles with diameters of 105 A. Naturally occurring mutations appear to favor the belt model for the 105 A particles, while the 150 A HDL particles favor the presence of at least one hairpin.

Apolipoprotein A-I↗

PubMed2002

Atomic structures of human dihydrofolate reductase complexed with NADPH and two lipophilic antifolates at 1.09 a and 1.05 a resolution.

The crystal structures of two human dihydrofolate reductase (hDHFR) ternary complexes, each with bound NADPH cofactor and a lipophilic antifolate inhibitor, have been determined at atomic resolution. The potent inhibitors 6-([5-quinolylamino]methyl)-2,4-diamino-5-methylpyrido[2,3-d]pyrimidine (SRI-9439) and (Z)-6-(2-[2,5-dimethoxyphenyl]ethen-1-yl)-2,4-diamino-5-methylpyrido[2,3-d]pyrimidine (SRI-9662) were developed at Southern Research Institute against Toxoplasma gondii DHFR-thymidylate synthase. The 5-deazapteridine ring of each inhibitor adopts an unusual puckered conformation that enables the formation of identical contacts in the active site. Conversely, the quinoline and dimethoxybenzene moieties exhibit distinct binding characteristics that account for the differences in inhibitory activity. In both structures, a salt-bridge is formed between Arg70 in the active site and Glu44 from a symmetry-related molecule in the crystal lattice that mimics the binding of methotrexate to DHFR.

Amino Acid Sequence↗

PubMedDate not supplied

Application of machine learning to improve the results of high-throughput docking against the HIV-1 protease.

We have previously reported that the application of a Laplacian-modified naive Bayesian (NB) classifier may be used to improve the ranking of known inhibitors from a random database of compounds after High-Throughput Docking (HTD). The method relies upon the frequency of substructural features among the active and inactive compounds from 2D fingerprint information of the compounds. Here we present an investigation of the role of extended connectivity fingerprints in training the NB classifier against HTD studies on the HIV-1 protease using three docking programs: Glide, FlexX, and GOLD. The results show that the performance of the NB classifier is due to the presence of a large number of features common to the set of known active compounds rather than a single structural or substructural scaffold. We demonstrate that the Laplacian-modified naive Bayesian classifier trained with data from high-throughput docking is superior at identifying active compounds from a target database in comparison to conventional two-dimensional substructure search methods alone.

Algorithms↗

PubMedDate not supplied

Improved naïve Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction.

We have implemented a naïve Bayesian classifier which models continuous numerical data using a Gaussian distribution. Several cases of interest in the area of absorption, distribution, metabolism, and excretion prediction are presented which demonstrate that this approach is superior to the implementation of naïve Bayesian classifiers in which continuous chemical descriptors are modeled as binary data. We demonstrate that this enhanced performance, upon comparison with other implementations, is independent of the descriptor sets chosen. We also compare the performance of three implementations of naïve Bayesian classifiers with other previously described models.

Bayes Theorem↗