Tectonics of a K+ channel: The importance of the N-terminus for channel gating

AbstractThe small K+ channel Kcv represents the pore module of complex potassium channels. It was found that its gating can be modified by sensor domains, which are N-terminally coupled to the pore. This implies that the short N-terminus of the channel can transmit conformational changes from upstream sensors to the channel gates. To understand the functional role of the N-terminus in the context of the entire channel protein, we apply combinatorial screening of the mechanical coupling and long-range interactions in the Kcv potassium channel by reduced molecular models. The dynamics and mechanical connections in the channel complex show that the N-terminus is indeed mechanically connected to the pore domain. This includes a long rang coupling to the pore and the inner and outer transmembrane domains. Since the latter domains host the two gates of the channel, the data support the hypothesis that mechanical perturbation of the N-terminus can be transmitted to the channel gates. This effect is solely determined by the topology of the channel; sequence details only have an implicit effect on the coarse-grained dynamics via the fold and not through biochemical details at a smaller scale. This observation has important implications for engineering of synthetic channels on the basis of a K+ channel pore

Computing and visually analyzing mutual information in molecular co-evolution

<p>Abstract</p> <p>Background</p> <p>Selective pressure in molecular evolution leads to uneven distributions of amino acids and nucleotides. In fact one observes correlations among such constituents due to a large number of biophysical mechanisms (folding properties, electrostatics, ...). To quantify these correlations the mutual information -after proper normalization - has proven most effective. The challenge is to navigate the large amount of data, which in a study for a typical protein cannot simply be plotted.</p> <p>Results</p> <p>To visually analyze mutual information we developed a matrix visualization tool that allows different views on the mutual information matrix: filtering, sorting, and weighting are among them. The user can interactively navigate a huge matrix in real-time and search e.g., for patterns and unusual high or low values. A computation of the mutual information matrix for a sequence alignment in FASTA-format is possible. The respective stand-alone program computes in addition proper normalizations for a null model of neutral evolution and maps the mutual information to <it>Z</it>-scores with respect to the null model.</p> <p>Conclusions</p> <p>The new tool allows to compute and visually analyze sequence data for possible co-evolutionary signals. The tool has already been successfully employed in evolutionary studies on HIV1 protease and acetylcholinesterase. The functionality of the tool was defined by users using the tool in real-world research. The software can also be used for visual analysis of other matrix-like data, such as information obtained by DNA microarray experiments. The package is platform-independently implemented in <monospace>Java</monospace> and free for academic use under a GPL license.</p

BioPhysConnectoR: Connecting Sequence Information and Biophysical Models

<p>Abstract</p> <p>Background</p> <p>One of the most challenging aspects of biomolecular systems is the understanding of the coevolution in and among the molecule(s).</p> <p>A complete, theoretical picture of the selective advantage, and thus a functional annotation, of (co-)mutations is still lacking. Using sequence-based and information theoretical inspired methods we can identify coevolving residues in proteins without understanding the underlying biophysical properties giving rise to such coevolutionary dynamics. Detailed (atomistic) simulations are prohibitively expensive. At the same time reduced molecular models are an efficient way to determine the reduced dynamics around the native state. The combination of sequence based approaches with such reduced models is therefore a promising approach to annotate evolutionary sequence changes.</p> <p>Results</p> <p>With the <monospace>R</monospace> package <monospace>BioPhysConnectoR</monospace> we provide a framework to connect the information theoretical domain of biomolecular sequences to biophysical properties of the encoded molecules - derived from reduced molecular models. To this end we have integrated several fragmented ideas into one single package ready to be used in connection with additional statistical routines in <monospace>R</monospace>. Additionally, the package leverages the power of modern multi-core architectures to reduce turn-around times in evolutionary and biomolecular design studies. Our package is a first step to achieve the above mentioned annotation of coevolution by reduced dynamics around the native state of proteins.</p> <p>Conclusions</p> <p><monospace>BioPhysConnectoR</monospace> is implemented as an <monospace>R</monospace> package and distributed under GPL 2 license. It allows for efficient and perfectly parallelized functional annotation of coevolution found at the sequence level.</p

Targeting Drug Resistance in EGFR with Covalent Inhibitors: A Structure-Based Design Approach

Receptor tyrosine kinases represent one of the prime targets in cancer therapy, as the dysregulation of these elementary transducers of extracellular signals, like the epidermal growth factor receptor (EGFR), contributes to the onset of cancer, such as non-small cell lung cancer (NSCLC). Strong efforts were directed to the development of irreversible inhibitors and led to compound CO-1686, which takes advantage of increased residence time at EGFR by alkylating Cys797 and thereby preventing toxic effects. Here, we present a structure-based approach, rationalized by subsequent computational analysis of conformational ligand ensembles in solution, to design novel and irreversible EGFR inhibitors based on a screening hit that was identified in a phenotype screen of 80 NSCLC cell lines against approximately 1500 compounds. Using protein X-ray crystallography, we deciphered the binding mode in engineered cSrc (T338M/S345C), a validated model system for EGFR-T790M, which constituted the basis for further rational design approaches. Chemical synthesis led to further compound collections that revealed increased biochemical potency and, in part, selectivity toward mutated (L858R and L858R/T790M) vs nonmutated EGFR. Further cell-based and kinetic studies were performed to substantiate our initial findings. Utilizing proteolytic digestion and nano-LC-MS/MS analysis, we confirmed the alkylation of Cys797

Actively personalized vaccination trial for newly diagnosed glioblastoma

Experimentele farmacotherapi