Novel technologies for virtual screening

Lengauer, Thomas; Lemmen, Christian; Rarey, Matthias; Zimmermann, Marc

doi:10.1016/s1359-6446(04)02939-3

Cited by 165 publications

(140 citation statements)

References 58 publications

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“…In this study, we focus on this last model-refinement step. Improvement of the accuracy of comparative models is very important because accurate comparative models potentially can be used for many applications, such as virtual drug scanning (9), molecular replacement (10), and function prediction (11). Refinement is particularly important when the sequence identity between a target protein and the template protein is Ͻ30% (12), because models built by using current methods generally have rms deviations (rmsd) of Ͼ1.5 Å (13).…”

mentioning

confidence: 99%

Improvement of comparative model accuracy by free-energy optimization along principal components of natural structural variation

Qian

Ortíz

Baker

2004

Proc. Natl. Acad. Sci. U.S.A.

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Accurate high-resolution refinement of protein structure models is a formidable challenge because of the delicate balance of forces in the native state, the difficulty in sampling the very large number of alternative tightly packed conformations, and the inaccuracies in current force fields. Indeed, energy-based refinement of comparative models generally leads to degradation rather than improvement in model quality, and, hence, most current comparative modeling procedures omit physically based refinement. However, despite their inaccuracies, current force fields do contain information that is orthogonal to the evolutionary information on which comparative models are based, and, hence, refinement might be able to improve comparative models if the space that is sampled is restricted sufficiently so that false attractors are avoided. Here, we use the principal components of the variation of backbone structures within a homologous family to define a small number of evolutionarily favored sampling directions and show that model quality can be improved by energy-based optimization along these directions.W ith the progression of structural genomics initiatives (1-3), comparative modeling has become an increasingly important method for building protein structure models (4, 5). After a suitable structure template is chosen, accurate comparative modeling requires a correct alignment between the target protein sequence and the template sequence, an accurate method for modeling the loops (the insertions and deletions in an alignment) and side chains, and, finally, a method for refining the coordinates derived from the template structure toward those of the true native structure (6-8). In this study, we focus on this last model-refinement step. Improvement of the accuracy of comparative models is very important because accurate comparative models potentially can be used for many applications, such as virtual drug scanning (9), molecular replacement (10), and function prediction (11). Refinement is particularly important when the sequence identity between a target protein and the template protein is Ͻ30% (12), because models built by using current methods generally have rms deviations (rmsd) of Ͼ1.5 Å (13).However, high-resolution refinement is as formidable as it is important. This difficulty is due to both the large size of conformational space and the delicate balance of forces in the native state. Indeed, in the recent CASP5 experiment (The 5th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction), most refined structures had larger rmsd to the native structure than the starting template backbone conformation (7). High-resolution refinement is thus a very stringent test of accuracy that perhaps no current force field satisfies.Progress on this very important but very challenging problem may be facilitated by focusing on more constrained and thus more tractable refinement problems. We were led to thinking about such problems by the observation that a refinement protocol that d...

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mentioning

confidence: 99%

Improvement of comparative model accuracy by free-energy optimization along principal components of natural structural variation

Qian

Ortíz

Baker

2004

Proc. Natl. Acad. Sci. U.S.A.

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“…Finally, in this work we specialize k (1) to its zero extension parameterized by a maximum distance d, that is, we consider:…”

Section: Graph Kernelsmentioning

confidence: 99%

“…1 There are two fundamental approaches: target-based and ligand-based. One can use docking to estimate affinity only if the 3D structure of the target protein is available in sufficient detail.…”

Section: Introductionmentioning

confidence: 99%

Molecular Graph Augmentation with Rings and Functional Groups

Grave

Costa

2010

J. Chem. Inf. Model.

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Molecular graphs are a compact representation of molecules, but may be too concise to obtain optimal generalization performance from graph-based machine learning algorithms. Over centuries, chemists have learned what are the important functional groups in molecules. This knowledge is normally not manifest in molecular graphs. In this paper, we introduce a simple method to incorporate this type of background knowledge: we insert additional vertices with corresponding edges for each functional group and ring structure identified in the molecule.We present experimental evidence that, on a wide range of ligand-based tasks and data sets, the proposed augmentation method improves the predictive performance over several graph kernel based QSAR models. When the augmentation technique is used with the recent Pairwise Maximal Common Subgraphs Kernel, we achieve a significant improvement over the current state-of-the-art on the NCI-60 cancer data set in 28 out of 60 cell lines, with the other 32 cell lines showing no significant difference in accuracy. Finally, on the Bursi mutagenicity data set, we obtain near-optimal predictions.

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“…There are, fundamentally, two approaches to VS studies: i. structure-based virtual screening (SBVS), which requires knowledge of the 3D structures of target proteins to prioritize compounds by their complementarity to the binding site; and, ii. ligandbased virtual screening (LBVS), where no information on the protein is needed, instead, compounds known to bind to the protein are used as queries to search databases for new molecules possessing biological activity [21][22][23][24][25]. Fig.…”

Section: Virtual Screeningmentioning

confidence: 99%

Virtual Screening and Its Integration with Modern Drug Design Technologies

Güido¹,

Oliva²,

Andricopulo

2008

CMC

165

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Drug discovery is a highly complex and costly process, which demands integrated efforts in several relevant aspects involving innovation, knowledge, information, technologies, expertise, R&D investments and management skills. The shift from traditional to genomics-and proteomics-based drug research has fundamentally transformed key R&D strategies in the pharmaceutical industry addressed to the design of new chemical entities as drug candidates against a variety of biological targets. Therefore, drug discovery has moved toward more rational strategies based on our increasing understanding of the fundamental principles of protein-ligand interactions. The combination of available knowledge of several 3D protein structures with hundreds of thousands of small-molecules have attracted the attention of scientists from all over the world for the application of structure-and ligand-based drug design approaches. In this context, virtual screening technologies have largely enhanced the impact of computational methods applied to chemistry and biology and the goal of applying such methods is to reduce large compound databases and to select a limited number of promising candidates for drug design. This review provides a perspective of the utility of virtual screening in drug design and its integration with other important drug discovery technologies such as high-throughput screening (HTS) and QSAR, highlighting the present challenges, limitations, and future perspectives in medicinal chemistry.

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Novel technologies for virtual screening

Cited by 165 publications

References 58 publications

Improvement of comparative model accuracy by free-energy optimization along principal components of natural structural variation

Improvement of comparative model accuracy by free-energy optimization along principal components of natural structural variation

Molecular Graph Augmentation with Rings and Functional Groups

Virtual Screening and Its Integration with Modern Drug Design Technologies

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