Biomedical subjects

Dongqing Wei

Publications and source records attributed to Dongqing Wei.

6 recordsLinked to original sources

PubMed2006

Structural and vibrational properties of solid nitromethane under high pressure by density functional theory.

The structural, vibrational, and electronic properties of solid nitromethane under hydrostatic pressure of up to 20 GPa have been studied using density functional theory. The changes of cell volume, the lattice constants, and the molecular geometry of solid nitromethane under hydrostatic loading are examined, and the bulk modulus B0 and its pressure derivative B0' are fitted from the volume-pressure relation. Our theoretical results are compared with available experiments. The change of electron band gap of nitromethane under high pressure is also discussed. Based on the optimized crystal structures, the vibrational frequencies for the internal and lattice modes of the nitromethane crystal at ambient and high pressures are computed, and the pressure-induced frequency shifts of these modes are discussed.

Chemistry, Physical↗

PubMed2005

Molecular modeling and chemical modification for finding peptide inhibitor against severe acute respiratory syndrome coronavirus main proteinase.

Severe acute respiratory syndrome (SARS) is a respiratory disease caused by a newly found virus, called SARS coronavirus. In this study, the cleavage mechanism of the SARS coronavirus main proteinase (Mpro or 3CLpro) on the octapeptide NH2-AVLQ downward arrowSGFR-COOH was investigated using molecular mechanics and quantum mechanics simulations based on the experimental structure of the proteinase. It has been observed that the catalytic dyad (His-41/Cys-145) site between domains I and II attracts the pi electron density from the peptide bond Gln-Ser, increasing the positive charge on C(CO) of Gln and the negative charge on N(NH) of Ser, so as to weaken the Gln-Ser peptide bond. The catalytic functional group is the imidazole group of His-41 and the S in Cys-145. Ndelta1 on the imidazole ring plays the acid-base catalytic role. Based on the "distorted key theory" [K.C. Chou, Anal. Biochem. 233 (1996) 1-14], the possibility to convert the octapeptide to a competent inhibitor has been studied. It has been found that the chemical bond between Gln and Ser will become much stronger and no longer cleavable by the SARS enzyme after either changing the carbonyl group CO of Gln to CH2 or CF2 or changing the NH of Ser to CH2 or CF2. The octapeptide thus modified might become an effective inhibitor or a potential drug candidate against SARS.

Acute Disease↗

PubMed2005

Assessment of chemical libraries for their druggability.

High throughput virtual screening is acknowledged as the initial means for identifying hit compounds that will be eventually transformed to leads or drug candidates. To improve quality of screening, it is essential to have powerful methods for the analysis of the compound databases. For this purpose, we have developed a novel and practical scoring function to assess the druggability of compounds. The proposed function consists of 12 metrics that take into account physical, chemical and structural properties as well as the presence of undesirable functional groups. We have applied this 12-metric scoring function to 44 different databases that include more than 3.8 million compounds, which are commercially available. The overall quality of each database was evaluated according to the score and rank measured by our 12-metric function. Our findings suggest that, the majority of compounds that do not satisfy druggable rules do so due to high molecular weight, high logP values and the presence of reactive functional groups.

Chemistry, Pharmaceutical↗

PubMed2005

Selection of molecular descriptors with artificial intelligence for the understanding of HIV-1 protease peptidomimetic inhibitors-activity.

Quantitative Structure Activity Relationship (QSAR) techniques are used routinely by computational chemists in drug discovery and development to analyze datasets of compounds. Quantitative numerical methods like Partial Least Squares (PLS) and Artificial Neural Networks (ANN) have been used on QSAR to establish correlations between molecular properties and bioactivity. However, ANN may be advantageous over PLS because it considers the interrelations of the modeled variables. This study focused on the HIV-1 Protease (HIV-1 Pr) inhibitors belonging to the peptidomimetic class of compounds. The main objective was to select molecular descriptors with the best predictive value for antiviral potency (Ki). PLS and ANN were used to predict Ki activity of HIV-1 Pr inhibitors and the results were compared. To address the issue of dimensionality reduction, Genetic Algorithms (GA) were used for variable selection and their performance was compared against that of ANN. Finally, the structure of the optimum ANN achieving the highest Pearson's-R coefficient was determined. On the basis of Pearson's-R, PLS and ANN were compared to determine which exhibits maximum performance. Training and validation of models was performed on 15 random split sets of the master dataset consisted of 231 compounds. For each compound 192 molecular descriptors were considered. The molecular structure and constant of inhibition (Ki) were selected from the NIAID database. Study findings suggested that non-covalent interactions such as hydrophobicity, shape and hydrogen bonding describe well the antiviral activity of the HIV-1 Pr compounds. The significance of lipophilicity and relationship to HIV-1 associated hyperlipidemia and lipodystrophy syndrome warrant further investigation.

Algorithms↗

PubMed2005

Application of bioinformatics in search for cleavable peptides of SARS-CoV M(pro) and chemical modification of octapeptides.

According to the "distorted key" theory as elaborated in a review article years ago (Chou, K.C.: Analytical Biochemistry, 1996, 233, 1-14), the knowledge of the cleavable peptides by SARS-CoV M(pro) (severe acute respiratory syndrome coronavirus main proteinase) can provide very useful insights on developing drugs against SARS. In view of this, the softwares, ZCURVE_CoV 1.0 and ZCURVE_CoV 2.0 (http://tubic.tju.edu.cn/sars/), developed recently for SARS-Coronavirus are used to analyze the 36 complete SARS-Coronavirus RNA sequences in the gene bank NCBI (http://www.ncbi.nlm.nih.gov/) from different sources for protein coding genes, and to search for the cleavage sites of SARS-CoV M(pro) in polyproteins pp1a and pp1ab. A total of 396 cleavage points are found in the 36 SARS-Coronavirus and 11 cleavable octapeptides abstracted from the 396 cleavage sites. The statistical distributions of amino acids for the cleavable octapeptides at the subsites R4, R3, R2, R1, R1', R2', R3' and R4' are calculated. The cleavage-specific positions are on R2, R1 and R1', and the positions R3 and R4 are featured by some certain specificity for SARS-CoV M(pro). The structural characters of amino acid residues around the cleavage-specific positions are discussed. Two most promising octapeptides, i.e., NH(2)-ATLQ downward arrowAIAS-COOH and NH(2)-ATLQ downward arrowAENV-COOH, are selected to be the candidates for chemical modification, converting into the inhibitors of SARS-CoV M(pro). A possible strategy to convert a cleavable octapeptide by SARS enzyme into a drug candidate against SARS is elucidated.

Amino Acid Sequence↗

PubMed2003

Correlations of amino acids in proteins.

A correlation analysis among 20 amino acids is performed for four protein structural classes (alpha, beta, alpha/beta, and alpha+beta) in a total of 204 proteins. The correlation relationships among amino acids can be classified into the following four types: (1) strong positive correlation, (2) strong negative correlation, (3) weak correlation, and (4) no correlation. The correlation relationships are different for different proteins and are correlated with the features of their structural classes. The amino acids with the weak correlation relationship can be treated as the independent basis functions for the space where proteins are defined. The amino acids with large correlation coefficients are linear correlative with each other and they are not independent. The strong correlation among amino acids reflects their mutual constrained relationship, as exhibited by their relevant structural features. The information obtained through the correlation analysis is used for predicting protein structural classes and a better prediction quality is obtained than that by the simple geometry distance methods without taking into account the correlation effects.

Amino Acids↗