Signaling in the crowded cell

High-resolution technologies have clarified some of the principles underlying cellular actions. However, understanding how cells receive, communicate, and respond to signals is still challenging. Questions include how efficient regulation of assemblies, which execute cell actions at the nanoscales, transmits productively at micrometer scales, especially considering the crowded environment, and how the cell organization makes it happen. Here, we describe how cells can navigate long-range diffusion-controlled signaling via association/dissociation of spatially proximal entities. Dynamic clusters can span the cell, engaging in most signaling steps. Effective local concentration, allostery, scaffolding, affinities, and the chemical and mechanical properties of the macromolecules and the environment play key roles. Signaling strength and duration matter, for example, deciding if a mutation promotes cancer or developmental syndromes.

A Hill type equation can predict target gene expression driven by p53 pulsing

Many factors determine target gene expression dynamics under p53 pulsing. In this study, I sought to determine the mechanism by which duration, frequency, binding affinity and maximal transcription rate affect the expression dynamics of target genes. Using an analytical method to solve a simple model, I found that the fold change of target gene expression increases relative to the number of p53 pulses, and the optimal frequency, 0.18 h-1 , from two real p53 pulses drives the maximal fold change with a decay rate of 0.18 h-1 . Moreover, p53 pulses may also lead to a higher fold change than sustained p53. Finally, I discovered that a Hill-type equation, including these effect factors, can characterise target gene expression. The average error between the theoretical predictions and experiments was 23%. Collectively, this equation advances the understanding of transcription factor dynamics, where duration and frequency play a significant role in the fine regulation of target gene expression with higher binding affinity.

PI3K Driver Mutations: A Biophysical Membrane-Centric Perspective

Ras activates its effectors at the membrane. Active PI3Kα and its associated kinases/phosphatases assemble at membrane regions enriched in signaling lipids. In contrast, the Raf kinase domain extends into the cytoplasm and its assembly is away from the crowded membrane surface. Our structural membrane-centric outlook underscores the spatiotemporal principles of membrane and signaling lipids, which helps clarify PI3Kα activation. Here we focus on mechanisms of activation driven by PI3Kα driver mutations, spotlighting the PI3Kα double (multiple) activating mutations. Single mutations can be potent, but double mutations are stronger: their combination is specific, a single strong driver cannot fully activate PI3K, and two weak drivers may or may not do so. In contrast, two strong drivers may successfully activate PI3K, where one, for example, H1047R, modulates membrane interactions facilitating substrate binding at the active site (km) and the other, for example, E542K and E545K, reduces the transition state barrier (ka), releasing autoinhibition by nSH2. Although mostly unidentified, weak drivers are expected to be common, so we ask here how common double mutations are likely to be and why PI3Kα with double mutations responds effectively to inhibitors. We provide a structural view of hotspot and weak driver mutations in PI3Kα activation, explain their mechanisms, compare these with mechanisms of Raf activation, and point to targeting cell-specific, chromatin-accessible, and parallel (or redundant) pathways to thwart the expected emergence of drug resistance. Collectively, our biophysical outlook delineates activation and highlights the challenges of drug resistance.

Unraveling allosteric landscapes of allosterome with ASD

Allosteric regulation is one of the most direct and efficient ways to fine-tune protein function; it is induced by the binding of a ligand at an allosteric site that is topographically distinct from an orthosteric site. The Allosteric Database (ASD, available online at http://mdl.shsmu.edu.cn/ASD) was developed ten years ago to provide comprehensive information related to allosteric regulation. In recent years, allosteric regulation has received great attention in biological research, bioengineering, and drug discovery, leading to the emergence of entire allosteric landscapes as allosteromes. To facilitate research from the perspective of the allosterome, in ASD 2019, novel features were curated as follows: (i) >10 000 potential allosteric sites of human proteins were deposited for allosteric drug discovery; (ii) 7 human allosterome maps, including protease and ion channel maps, were built to reveal allosteric evolution within families; (iii) 1312 somatic missense mutations at allosteric sites were collected from patient samples from 33 cancer types and (iv) 1493 pharmacophores extracted from allosteric sites were provided for modulator screening. Over the past ten years, the ASD has become a central resource for studying allosteric regulation and will play more important roles in both target identification and allosteric drug discovery in the future.

Cracking the context-specific PI3K signaling code

Specificity in signal transduction is determined by the ability of cells to "encode" and subsequently "decode" different environmental signals. Akin to computer software, this "signaling code" governs context-dependent execution of cellular programs through modulation of signaling dynamics and can be corrupted by disease-causing mutations. Class IA phosphoinositide 3-kinase (PI3K) signaling is critical for normal growth and development and is dysregulated in human disorders such as benign overgrowth syndromes, cancer, primary immune deficiency, and metabolic syndrome. Despite decades of PI3K research, understanding of context-dependent regulation of the PI3K pathway and of the underlying signaling code remains rudimentary. Here, we review current knowledge on context-specific PI3K signaling and how technological advances now make it possible to move from a qualitative to quantitative understanding of this pathway. Insight into how cellular PI3K signaling is encoded or decoded may open new avenues for rational pharmacological targeting of PI3K-associated diseases. The principles of PI3K context-dependent signal encoding and decoding described here are likely applicable to most, if not all, major cell signaling pathways.