Biomedical subjects

Zhan Deng

Publications and source records attributed to Zhan Deng.

7 recordsLinked to original sources

PubMed2006

Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors.

Bridging chemical and biological space is the key to drug discovery and development. Typically, cheminformatics methods operate under the assumption that similar chemicals have similar biological activity. Ideally then, one could predict a drug's biological function(s) given only its chemical structure by similarity searching in libraries of compounds with known activities. In practice, effectively choosing a similarity metric is case dependent. This work compares both 2D and 3D chemical descriptors as tools for predicting the biological targets of ligand probes, on the basis of their similarity to reference molecules in a 46,000 compound, biologically annotated chemical database. Overall, we found that the 2D methods employed here outperform the 3D (88% vs 67% success) in correct target prediction. However, the 3D descriptors proved superior in cases of probes with low structural similarity to other compounds in the database (singletons). Additionally, the 3D method (FEPOPS) shows promise for providing pharmacophoric alignment of the small molecules' chemical features consistent with those seen in experimental ligand/ receptor complexes. These results suggest that querying annotated chemical databases with a systematic combination of both 2D and 3D descriptors will prove more effective than employing single methods.

Adenosine Triphosphate↗

PubMed2006

Knowledge-based design of target-focused libraries using protein-ligand interaction constraints.

Here we present a new strategy for designing and filtering potentially massive combinatorial libraries using structural information of a binding site. We have developed a variation of the structural interaction fingerprint (SIFt) named r-SIFt, which incorporates the binding interactions of variable fragments in a combinatorial library. This method takes into account the 3D structure of the active site of the target molecule and translates desirable ligand-target binding interactions into library filtering constraints. We show using the MAP kinase p38 as a test case that we can efficiently analyze and classify compounds on the basis of their abilities to interact with the target in the desired binding mode. On the basis of these classifications, decision tree models were generated using the molecular descriptors of the compounds as predictor variables. Our results suggest that r-SIFt coupled with the classification models should be a valuable tool for structure-based focusing of combinatorial chemical libraries.

Binding Sites↗

PubMed2006

Structural interaction fingerprints: a new approach to organizing, mining, analyzing, and designing protein-small molecule complexes.

The combination of advances in structure-based drug design efforts in the pharmaceutical industry in parallel with structural genomics initiatives in the public domain has led to an explosion in the number of structures of protein-small molecule complexes structures. This information has critical importance to both the understanding of the structural basis for molecular recognition in biological systems and the design of better drugs. A significant challenge exists in managing this vast amount of data and fully leveraging it. Here, we review our work to develop a simple, fast way to store, organize, mine, and analyze large numbers of protein-small molecule complexes. We illustrate the utility of the approach to the management of inhibitor complexes from the protein kinase family. Finally, we describe our recent efforts in applying this method to the design of target-focused chemical libraries.

Amino Acid Sequence↗

PubMed2006

A kinase-focused compound collection: compilation and screening strategy.

Lead identification by high-throughput screening of large compound libraries has been supplemented with virtual screening and focused compound libraries. To complement existing approaches for lead identification at Biogen Idec, a kinase-focused compound collection was designed, developed and validated. Two strategies were adopted to populate the compound collection: a ligand shape-based virtual screening and a receptor-based approach (structural interaction fingerprint). Compounds selected with the two approaches were cherry-picked from an existing high-throughput screening compound library, ordered from suppliers and supplemented with specific medicinal compounds from internal programs. Promising hits and leads have been generated from the kinase-focused compound collection against multiple kinase targets. The principle of the collection design and screening strategy was validated and the use of the kinase-focused compound collection for lead identification has been added to existing strategies.

Combinatorial Chemistry Techniques↗

PubMed2005

Interaction profiles of protein kinase-inhibitor complexes and their application to virtual screening.

A major challenge facing structure-based drug discovery efforts is how to leverage the massive amount of experimental (X-ray and NMR) and virtual structural information generated from drug discovery projects. Many important drug targets have large numbers of protein-inhibitor complexes, necessitating tools to compare and contrast their similarities and differences. This information would be valuable for understanding potency and selectivity of inhibitors and could be used to define target constraints to assist virtual screening. We describe a profile-based approach that enables us to capture the conservation of interactions between a set of protein-ligand receptor complexes. The use of profiles provides a sensitive means to compare multiple inhibitors binding to a drug target. We demonstrate the utility of profile-based analysis of small molecule complexes from the protein-kinase family to identify similarities and differences in binding of ATP, p38, and CDK2 compounds to kinases and how these profiles can be applied to differentiate the selectivity of these inhibitors. Importantly, our virtual screening results demonstrate superior enrichment of kinase inhibitors using profile-based methods relative to traditional scoring functions. Interaction-based analysis should provide a valuable tool for understanding inhibitor binding to other important drug targets.

Adenosine Triphosphate↗

PubMed2004

Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions.

Representing and understanding the three-dimensional (3D) structural information of protein-ligand complexes is a critical step in the rational drug discovery process. Traditional analysis methods are proving inadequate and inefficient in dealing with the massive amount of structural information being generated from X-ray crystallography, NMR, and in silico approaches such as structure-based docking experiments. Here, we present SIFt (structural interaction fingerprint), a novel method for representing and analyzing 3D protein-ligand binding interactions. Key to this approach is the generation of an interaction fingerprint that translates 3D structural binding information from a protein-ligand complex into a one-dimensional binary string. Each fingerprint represents the "structural interaction profile" of the complex that can be used to organize, analyze, and visualize the rich amount of information encoded in ligand-receptor complexes and also to assist database mining. We have applied SIFt to tackle three common tasks in structure-based drug design. The first involved the analysis and organization of a typical set of results generated from a docking study. Using SIFt, docking poses with similar binding modes were identified, clustered, and subsequently compared with conventional scoring function information. A second application of SIFt was to analyze approximately 90 known X-ray crystal structures of protein kinase-inhibitor complexes obtained from the Protein Databank. Using SIFt, we were able to organize the structures and reveal striking similarities and diversity between their small molecule binding interactions. Finally, we have shown how SIFt can be used as an effective molecular filter during the virtual chemical library screening process to select molecules with desirable binding mode(s) and/or desirable interaction patterns with the protein target. In summary, SIFt shows promise to fully leverage the wealth of information being generated in rational drug design.

Binding Sites↗

PubMedDate not supplied

"Bayes affinity fingerprints" improve retrieval rates in virtual screening and define orthogonal bioactivity space: when are multitarget drugs a feasible concept?

Conventional similarity searching of molecules compares single (or multiple) active query structures to each other in a relative framework, by means of a structural descriptor and a similarity measure. While this often works well, depending on the target, we show here that retrieval rates can be improved considerably by incorporating an external framework describing ligand bioactivity space for comparisons ("Bayes affinity fingerprints"). Structures are described by Bayes scores for a ligand panel comprising about 1000 activity classes extracted from the WOMBAT database. The comparison of structures is performed via the Pearson correlation coefficient of activity classes, that is, the order in which two structures are similar to the panel activity classes. Compound retrieval on a recently published data set could be improved by as much as 24% relative (9% absolute). Knowledge about the shape of the "bioactive chemical universe" is thus beneficial to identifying similar bioactivities. Principal component analysis was employed to further analyze activity space with the objective to define orthogonal ligand bioactive chemical space, leading to nine major (roughly orthogonal) activity axes. Employing only those nine activity classes, retrieval rates are still comparable to original Bayes affinity fingerprints; thus, the concept of orthogonal bioactive ligand chemical space was validated as being an information-rich but low-dimensional representation of bioactivity space. Correlations between activity classes are a major determinant to gauge whether the desired multitarget activity of drugs is (on the basis of current knowledge) a feasible concept because it measures the extent to which activities can be optimized independently, or only by strongly influencing one another.

Algorithms↗