Biomedical subjects

Shuxing Zhang

Publications and source records attributed to Shuxing Zhang.

5 recordsLinked to original sources

PubMed2006

Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces.

Novel geometrical chemical descriptors have been derived on the basis of the computational geometry of protein-ligand interfaces and Pauling atomic electronegativities (EN). Delaunay tessellation has been applied to a diverse set of 517 X-ray characterized protein-ligand complexes yielding a unique collection of interfacial nearest neighbor atomic quadruplets for each complex. Each quadruplet composition was characterized by a single descriptor calculated as the sum of the EN values for the four participating atom types. We termed these simple descriptors generated from atomic EN values and derived with the Delaunay Tessellation the ENTess descriptors and used them in the variable selection k-nearest neighbor quantitative structure-binding affinity relationship (QSBR) studies of 264 diverse protein-ligand complexes with known binding constants. Twenty-four complexes with chemically dissimilar ligands were set aside as an independent validation set, and the remaining dataset of 240 complexes was divided into multiple training and test sets. The best models were characterized by the leave-one-out cross-validated correlation coefficient q(2) as high as 0.66 for the training set and the correlation coefficient R(2) as high as 0.83 for the test set. The high predictive power of these models was confirmed independently by applying them to the validation set of 24 complexes yielding R(2) as high as 0.85. We conclude that QSBR models built with the ENTess descriptors can be instrumental for predicting the binding affinity of receptor-ligand complexes.

Computer Simulation↗

PubMed2005

An alternative domain near the nucleotide-binding site of Drosophila muscle myosin affects ATPase kinetics.

In Drosophila melanogaster expression of muscle myosin heavy chain isoforms occurs by alternative splicing of transcripts from a single gene. The exon 7 domain is one of four variable regions in the catalytic head and is located near the nucleotide-binding site. To ascribe a functional role to this domain, we created two chimeric myosin isoforms (indirect flight isoform-exon 7a and embryonic-exon 7d) that differ from the native indirect flight muscle and embryonic body-wall muscle isoforms only in the exon 7 region. Germline transformation and subsequent expression of the chimeric myosins in the indirect flight muscle of myosin-null Drosophila allowed us to purify the myosin for in vitro studies and to assess in vivo structure and function of transgenic muscles. Intriguingly, in vitro experiments show the exon 7 domain modulates myosin ATPase activity but has no effect on actin filament velocity, a novel result compared to similar studies with other Drosophila variable exons. Transgenic flies expressing the indirect flight isoform-exon 7a have normal indirect flight muscle structure, and flight and jump ability. However, expression of the embryonic-exon 7d chimeric isoform yields flightless flies that show improvements in both the structural stability of the indirect flight muscle and in locomotor abilities as compared to flies expressing the embryonic isoform. Overall, our results suggest the exon 7 domain participates in the regulation of the attachment of myosin to actin in order to fine-tune the physiological properties of Drosophila myosin isoforms.

Actins↗

PubMed2004

Evaluation of the relative stability of liganded versus ligand-free protein conformations using Simplicial Neighborhood Analysis of Protein Packing (SNAPP) method.

Many proteins change their conformation upon ligand binding. For instance, bacterial periplasmic binding proteins (bPBPs), which transport nutrients into the cytoplasm, generally consist of two globular domains connected by strands, forming a hinge. During ligand binding, hinge motion changes the conformation from the open to the closed form. Both forms can be crystallized without a ligand, suggesting that the energy difference between them is small. We applied Simplicial Neighborhood Analysis of Protein Packing (SNAPP) as a method to evaluate the relative stability of open and closed forms in bPBPs. Using united residue representation of amino acids, SNAPP performs Delaunay tessellation of the protein, producing an aggregate of space-filling, irregular tetrahedra with nearest neighbor residues at the vertices. The SNAPP statistical scoring function is derived from log-likelihood scores for all possible quadruplet compositions of amino acids found in a representative subset of the Protein Data Bank, and the sum of the scores for a given protein provides the total SNAPP score. Results of scoring for bPBPs suggest that in most cases, the unliganded form is more stable than the liganded form, and this conclusion is corroborated by similar observations of other proteins undergoing conformation changes upon binding their ligands. The results of these studies suggest that the SNAPP method can be used to predict the relative stability of accessible protein conformations. Furthermore, the SNAPP method allows delineation of the role of individual residues in protein stabilization, thereby providing new testable hypotheses for rational site-directed mutagenesis in the context of protein engineering.

Amino Acids↗

PubMedDate not supplied

Chemometric analysis of ligand receptor complementarity: identifying Complementary Ligands Based on Receptor Information (CoLiBRI).

We have developed a novel structure-based chemoinformatics approach to search for Complimentary Ligands Based on Receptor Information (CoLiBRI). CoLiBRI is based on the representation of both receptor binding sites and their respective ligands in a space of universal chemical descriptors. The binding site atoms involved in the interaction with ligands are identified by the means of a computational geometry technique known as Delaunay tessellation as applied to X-ray characterized ligand-receptor complexes. TAE/RECON multiple chemical descriptors are calculated independently for each ligand as well as for its active site atoms. The representation of both ligands and active sites using chemical descriptors allows the application of well-known chemometric techniques in order to correlate chemical similarities between active sites and their respective ligands. We have established a protocol to map patterns of nearest neighbor active site vectors in a multidimensional TAE/RECON space onto those of their complementary ligands and vice versa. This protocol affords the prediction of a virtual complementary ligand vector in the ligand chemical space from the position of a known active site vector. This prediction is followed by chemical similarity calculations between this virtual ligand vector and those calculated for molecules in a chemical database to identify real compounds most similar to the virtual ligand. Consequently, the knowledge of the receptor active site structure affords straightforward and efficient identification of its complementary ligands in large databases of chemical compounds using rapid chemical similarity searches. Conversely, starting from the ligand chemical structure, one may identify possible complementary receptor cavities as well. We have applied the CoLiBRI approach to a data set of 800 X-ray characterized ligand-receptor complexes in the PDBbind database. Using a k nearest neighbor (kNN) pattern recognition approach and variable selection, we have shown that knowledge of the active site structure affords identification of its complimentary ligand among the top 1% of a large chemical database in over 90% of all test active sites when a binding site of the same protein family was present in the training set. In the case where test receptors are highly dissimilar and not present among the receptor families in the training set, the prediction accuracy is decreased; however, CoLiBRI was still able to quickly eliminate 75% of the chemical database as improbable ligands. CoLiBRI affords rapid prefiltering of a large chemical database to eliminate compounds that have little chance of binding to a receptor active site.

Algorithms↗

PubMedDate not supplied

A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models.

A novel automated lazy learning quantitative structure-activity relationship (ALL-QSAR) modeling approach has been developed on the basis of the lazy learning theory. The activity of a test compound is predicted from a locally weighted linear regression model using chemical descriptors and the biological activity of the training set compounds most chemically similar to this test compound. The weights with which training set compounds are included in the regression depend on the similarity of those compounds to a test compound. We have applied the ALL-QSAR method to several experimental chemical data sets including 48 anticonvulsant agents with known ED50 values, 48 dopamine D1-receptor antagonists with known competitive binding affinities (Ki), and a Tetrahymena pyriformis data set containing 250 phenolic compounds with toxicity IGC50 values. When applied to database screening, models developed for anticonvulsant agents identified several known anticonvulsant compounds that were not only absent in the training set but highly chemically dissimilar to the training set compounds. This initial success indicates that ALL-QSAR can be further exploited as a general tool for accurate bioactivity prediction and database screening in drug design and discovery. Because of its local nature, the ALL-QSAR approach appears to be especially well-suited for the development of highly predictive models for the sparse or unevenly distributed data sets.

Database Management Systems↗