Seminars

Abstract

Connexin proteins form wide pores in plasma membrane and between adjacent cells. These gated pores are permeable to atomic ions and small molecules, thereby mediating electrical and molecular signaling. The intercellular channels (“gap junction channels”) permit cytosolic molecules such as IP3 and cAMP to pass between cells. The plasma membrane channels (“hemichannels”) play autocrine/paracrine roles by releasing molecules such as glutamate and ATP into the extracellular environment. The molecular signaling mediated by connexin channels is of critical biomedical importance, being intimately involved in development, normal physiology, and response to trauma and disease. Defects in connexin channels cause human pathologies. Although connexin channels as a class are permeable to a wide variety of small molecules, channels formed by each of the 21 human connexin isoforms have strikingly different ionic and molecular selectivities. The molecular permselectivity is not a simple function of pore width but depends on as yet uncharacterized interactions between specific permeants and the pore lumen of each connexin isoform; there is little correlation among channel unitary conductance, limiting pore diameter and/or charge selectivity. To help elucidate the mechanisms that influence and define selective molecular permeation, molecular dynamics simulations were applied to similar two molecules – one permeant and one not – in a connexin hemichannel. The results highlight issues and factors that come into play in selective molecular permeation of wide pores that are different from those that dominate permeation of atomic ions through ion-selective pores. These include mechanisms of selectivity involving low-energy interactions, and ‘‘permeant’’ and side-chain flexibility, orientation and anisotropy. A key element of the energetic landscape is the entropic contribution due to molecules that can occupy many configurations and orientations in a non-rigid water-filled pore. Pore width influences the energetic landscapes experienced by these molecules and differences between them, but other factors are strongly involved. Also, for both the permeant and impermeant test molecules, the computed energetic barriers extend through most of the pore, without significant binding (energy wells). The results suggest that this type of analysis may be useful in exploring the molecular basis by which connexin channels distinguish among (potential) permeating molecules, and how mutations may alter the permeation process.

Abstract

How do Ras isoforms attain oncogenic specificity at the membrane? Oncogenic KRas, HRas, and NRas differentially populate distinct cancers. How they selectively activate effectors and why is KRas4B the most prevalent are highly significant questions. We also ask how Ras activates its effectors, including Raf and PI3K lipid kinase, which are also among the most highly mutated proteins in cancer. Despite decades of studies, major mechanistic questions are still unanswered. Broadly, we aim to figure out the hallmarks of oncogenic signaling in the cell.

Abstract

While we know that protein sequence encodes structure, capturing this sequence-to-structure mapping computationally has been difficult. Particularly so because the space of structural possibilities appears immense and complex. We propose that this space should nevertheless be describable as a combination of discrete local structural patterns. We introduce the concept of a TERM (tertiary motif), which encapsulates the full structural environment around a given residue, and show that the protein structural universe is highly degenerate at the level of TERMs. In fact, only 650 TERMs describe over 50% of the structural database at sub-Angstrom resolution. We go on to show that such degeneracy enables the direct quantification of sequence-structure relationships. Local sequence models can be extracted for each TERM contained in a protein structure, based on the frequent reuse of TERMs in unrelated proteins, with the overall protein structure described as a combination of these models. We have begun to demonstrate the broad applicability of such a framework across a variety of applications: 1) protein design: we have either partially or fully redesigned multiple proteins using TERM data alone, as well as designed novel structures de novo (with experimental validation); 2) structure prediction: we found that TERM-based sequence statistics identify accurate models; 3) we have shown that mutational stability changes are predicted quantitatively from TERM data alone. Earlier findings of degeneracies in the protein structure (e.g., for secondary and super-secondary motifs), have greatly advanced computational structural biology. TERM-based mining of structural data is the next logical step that should provide further quantitative insights into sequence-structural relationships.