4,939 research outputs found
Computational structure‐based drug design: Predicting target flexibility
The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant
from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft
Enrichment and aggregation of topological motifs are independent organizational principles of integrated interaction networks
Topological network motifs represent functional relationships within and
between regulatory and protein-protein interaction networks. Enriched motifs
often aggregate into self-contained units forming functional modules.
Theoretical models for network evolution by duplication-divergence mechanisms
and for network topology by hierarchical scale-free networks have suggested a
one-to-one relation between network motif enrichment and aggregation, but this
relation has never been tested quantitatively in real biological interaction
networks. Here we introduce a novel method for assessing the statistical
significance of network motif aggregation and for identifying clusters of
overlapping network motifs. Using an integrated network of transcriptional,
posttranslational and protein-protein interactions in yeast we show that
network motif aggregation reflects a local modularity property which is
independent of network motif enrichment. In particular our method identified
novel functional network themes for a set of motifs which are not enriched yet
aggregate significantly and challenges the conventional view that network motif
enrichment is the most basic organizational principle of complex networks.Comment: 12 pages, 5 figure
Advances and Challenges in Protein-Ligand Docking
Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions—the three important aspects in protein-ligand docking. Challenges and possible future directions are discussed in the Conclusion
- …