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. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Software > Molecular Modeling
We present SS-map, a tool to visualize the secondary structure content of ensembles of proteins. When generating ensembles of intrinsically disordered proteins, we lose the understanding a single native structure gives for folded proteins. It then becomes difficult to visualize the composition of the ensembles or to detect transient helices such as MoRFs. Conformational propensities for single residues also hide the nature of cooperative structures. Here we show how SS-map describes folded and unfolded ensembles of some peptides and gives a new view of the ensembles used to describe intrinsically disordered proteins with residual structure in computational and NMR experiments. This tool is implemented in an open-source python code located at code.google.com/p/ss-map
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