Molecular Dynamics of Biomolecules through Direct Analysis of Dipolar Couplings

Residual dipolar couplings (RDCs) are important probes in structural biology, but their analysis is often complicated by the determination of an alignment tensor or its associated assumptions. We here apply the maximum entropy principle to derive a tensor-free formalism which allows for direct, dynamic analysis of RDCs and holds the classic tensor formalism as a special case. Specifically, the framework enables us to robustly analyze data regardless of whether a clear separation of internal and overall dynamics is possible. Such a separation is often difficult in the core subjects of current structural biology, which include multidomain and intrinsically disordered proteins as well as nucleic acids. We demonstrate the method is tractable and self-consistent and generalizes to data sets comprised of observations from multiple different alignment conditions

Molecular Determinants for Unphosphorylated STAT3 Dimerization Determined by Integrative Modeling

Signal transducer and activator of transcription factors (STATs) are proteins that can translocate into the nucleus, bind DNA, and activate gene transcription. STAT proteins play a crucial role in cell proliferation, apoptosis, and differentiation. The prevalent view is that STAT proteins are able to form dimers and bind DNA only upon phosphorylation of specific tyrosine residues in the transactivation domain. However, this paradigm has been questioned recently by the observation of dimers of unphosphorylated STATs (USTATs) by X-ray, Förster resonance energy transfer, and site-directed mutagenesis. A more complex picture of the dimerization process and of the role of the dimers is, thus, emerging. Here we present an integrated modeling study of STAT3, a member of the STAT family of utmost importance in cancer development and therapy, in which we combine available experimental data with several computational methodologies such as homology modeling, protein–protein docking, and molecular dynamics to build reliable atomistic models of USTAT3 dimers. The models generated with the integrative approach presented here were then validated by performing computational alanine scanning for all the residues in the protein–protein interface. These results confirmed the experimental observation of the importance of some of these residues (in particular Leu78 and Asp19) in the USTAT3 dimerization process. Given the growing importance of USTAT3 dimers in several cellular pathways, our models provide an important tool for studying the effects of pathological mutations at the molecular and/or atomistic level, and in the rational design of new inhibitors of dimerization