Biomedical subjects

Lisa Yan

Publications and source records attributed to Lisa Yan.

7 recordsLinked to original sources

PubMed2006

Molecular basis for the Cu2+ binding-induced destabilization of beta2-microglobulin revealed by molecular dynamics simulation.

According to experimental data, binding of the Cu(2+) ions destabilizes the native state of beta2-microglobulin (beta2m). The partial unfolding of the protein was generally considered an early step toward fibril formation in dialysis-related amyloidosis. Recent NMR studies have suggested that the destabilization of the protein might be achieved through increased flexibility upon Cu(2+) binding. However, the molecular mechanism of destabilization due to Cu(2+), its role in amyloid formation, and the relative contributions of different potential copper-binding sites remain unclear. To elucidate the effect of ion ligation at atomic detail, a series of molecular dynamics simulations were carried out on apo- and Cu(2+)-beta2m systems in explicit aqueous solutions, with varying numbers of bound ions. Simulations at elevated temperatures (360 K) provide detailed pictures for the process of Cu(2+)-binding-induced destabilization of the native structure at the nanosecond timescale, which are in agreement with experiments. Conformational transitions toward partially unfolded states were observed in protein solutions containing bound copper ions at His-31 and His-51, which is marked by an increase in the protein vibrational entropy, with TDeltaS(vibr) ranging from 30 to 69 kcal/mol. The binding of Cu(2+) perturbs the secondary structure and the hydrogen bonding pattern disrupts the native hydrophobic contacts in the neighboring segments, which include the beta-strand D2 and part of the beta-strand E, B, and C and results in greater exposure of the D-E loop and the B-C loop to the water environment. Analysis of the MD trajectories suggests that the changes in the hydrophobic environment near the copper-binding sites lower the barrier of conformational transition and stabilize the more disordered conformation. The results also indicate that the binding of Cu(2+) at His-13 has little effect on the conformational stability, whereas the copper-binding site His-31, and to a lesser extent His-51, are primarily responsible for the observed changes in the protein conformation and dynamics.

Cations, Divalent↗

PubMed2004

A hidden Markov model with molecular mechanics energy-scoring function for transmembrane helix prediction.

A range of methods has been developed to predict transmembrane helices and their topologies. Although most of these algorithms give good predictions, no single method consistently outperforms the others. However, combining different algorithms is one approach that can potentially improve the accuracy of the prediction. We developed a new method that initially uses a hidden Markov model to predict alternative models for membrane spanning helices in proteins. The algorithm subsequently identifies the best among models by ranking them using a novel scoring function based on the folding energy of transmembrane helical fragments. This folding of helical fragments and the incorporation into membrane is modeled using CHARMm, extended with the Generalized Born surface area solvent model (GBSA/IM) with implicit membrane. The combined method reported here, TMHGB significantly increases the accuracy of the original hidden Markov model-based algorithm.

Algorithms↗

PubMed2003

Assessment of putative protein targets derived from the SARS genome.

The ability to rapidly and reliably develop hypotheses on the function of newly discovered protein sequences requires systematic and comprehensive analysis. Such an analysis, embodied within the DS GeneAtlas pipeline, has been used to critically evaluate the severe acute respiratory syndrome (SARS) genome with the goal of identifying new potential targets for viral therapeutic intervention. This paper discusses several new functional hypotheses on the roles played by the constituent gene products of SARS, and will serve as an example of how such assignments can be developed or extended on other systems of interest.

Amino Acid Sequence↗

PubMed2003

An extensible and systematic force field, ESFF, for molecular modeling of organic, inorganic, and organometallic systems.

ESFF is a rule-based force field designed for modeling organic, inorganic, and organometallic systems. To cover this broad range of molecular systems, ESFF was developed in an extensible and systematic manner. Several unique features were introduced including pseudoangle and a dot product function representing torsion energy terms. The partial atomic charges that are topology-dependent are determined from ab initio (DFT) calculated electronegativity and hardness for valence orbitals. The van der Waals parameters are charge-dependent, and correlated with the ionization potential for atoms in various valence states. To obtain a set of well-defined and physically meaningful parameters, ESFF employs semiempirical rules to translate atomic-based parameters to parameters typically associated with a covalent valence force field. The atomic parameters depend not only on atom type, but also on internal type, thus resulting in a more accurate force field. This article presents the theory and the method used to develop the force field. The force field has been applied to molecular simulations of a wide variety of systems including nucleic acids, peptides, hydrocarbons, porphyrins, transition metal complexes, zeolites, and organometallic compounds. Agreement with the experimental results indicates that ESFF is a valuable tool in molecular simulations for understanding and predicting both crystal and gas phase molecular structures.

Algorithms↗

PubMed2003

Glutamate-115 renders specificity of human 11beta-hydroxysteroid dehydrogenase type 2 for the cofactor NAD+.

The renal 11beta-hydroxysteroid dehydrogenase (11beta-HSD) type 2 catalyzes the NAD(+)-dependent oxidation of the C11-alcohol on cortisol and corticosterone to yield inactive 11-ketosteroids. The lack of purified active enzyme complicates structure-function analyses of 11beta-HSD2. Here, we constructed a 3D-structural model of 11beta-HSD2, based on known 3D-structures of other short-chain dehydrogenases/reductases (SDR), and functionally analyzed 11beta-HSD2 mutants predicted to be involved in cofactor binding. Our 3D-model explains the preference for NAD(+) over NADP(+) by the coulombic repulsion between the adenosine ribose 2'-phosphate on NADP(+) and the carboxylate on Glu(115) and to steric hindrance with the side chain on Glu(115). Indeed, replacement of Glu(115) with serine or threonine, lacking repulsive charge and unfavorable steric interactions, showed only 3-fold preference for NAD(+), compared to 40-fold for wild-type 11beta-HSD2. Mutation of both Asp(91) and Glu(115) to serine raised NADP(+)-dependent activity to that with NAD(+), but caused reduced enzymatic activity. The 3D-model predicted that this is due to a loss of stabilizing interactions of Asp(91) with Cys(90), Glu(115), Asn(117) and Gly(120). Thus, predictions using the 3D-model combined with analysis of mutants allowed the identification of residues critical for NAD(+)-dependent activity of 11beta-HSD2.

11-beta-Hydroxysteroid Dehydrogenase Type 2↗

PubMed2002

Functional annotation of proteomic sequences based on consensus of sequence and structural analysis.

To maximise the assignment of function of the proteins encoded by a genome and to aid the search for novel drug targets, there is an emerging need for sensitive methods of predicting protein function on a genome-wide basis. GeneAtlas is an automated, high-throughput pipeline for the prediction of protein structure and function using sequence similarity detection, homology modelling and fold recognition methods. GeneAtlas is described in detail here. To test GeneAtlas, a 'virtual' genome was used, a subset of PDB structures from the SCOP database, in which the functional relationships are known. GeneAtlas detects additional relationships by building 3D models in comparison with the sequence searching method PSI-BLAST. Functionally related proteins with sequence identity below the twilight zone can be recognised correctly.

Consensus Sequence↗

PubMed2001

Derivation of class II force fields. VIII. Derivation of a general quantum mechanical force field for organic compounds.

A class II valence force field covering a broad range of organic molecules has been derived employing ab initio quantum mechanical "observables." The procedure includes selecting representative molecules and molecular structures, and systematically sampling their energy surfaces as described by energies and energy first and second derivatives with respect to molecular deformations. In this article the procedure for fitting the force field parameters to these energies and energy derivatives is briefly reviewed. The application of the methodology to the derivation of a class II quantum mechanical force field (QMFF) for 32 organic functional groups is then described. A training set of 400 molecules spanning the 32 functional groups was used to parameterize the force field. The molecular families comprising the functional groups and, within each family, the torsional angles used to sample different conformers, are described. The number of stationary points (equilibria and transition states) for these molecules is given for each functional group. This set contains 1324 stationary structures, with 718 minimum energy structures and 606 transition states. The quality of the fit to the quantum data is gauged based on the deviations between the ab initio and force field energies and energy derivatives. The accuracy with which the QMFF reproduces the ab initio molecular bond lengths, bond angles, torsional angles, vibrational frequencies, and conformational energies is then given for each functional group. Consistently good accuracy is found for these computed properties for the various types of molecules. This demonstrates that the methodology is broadly applicable for the derivation of force field parameters across widely differing types of molecular structures. Copyright 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1782-1800, 2001

Journal Article↗