A Computational Strategy to Select Optimized Protein Targets for Drug Development toward the Control of Cancer Diseases

<div><p>In this report, we describe a strategy for the optimized selection of protein targets suitable for drug development against neoplastic diseases taking the particular case of breast cancer as an example. We combined human interactome and transcriptome data from malignant and control cell lines because highly connected proteins that are up-regulated in malignant cell lines are expected to be suitable protein targets for chemotherapy with a lower rate of undesirable side effects. We normalized transcriptome data and applied a statistic treatment to objectively extract the sub-networks of down- and up-regulated genes whose proteins effectively interact. We chose the most connected ones that act as protein hubs, most being in the signaling network. We show that the protein targets effectively identified by the combination of protein connectivity and differential expression are known as suitable targets for the successful chemotherapy of breast cancer. Interestingly, we found additional proteins, not generally targeted by drug treatments, which might justify the extension of existing formulation by addition of inhibitors designed against these proteins with the consequence of improving therapeutic outcomes. The molecular alterations observed in breast cancer cell lines represent either driver events and/or driver pathways that are necessary for breast cancer development or progression. However, it is clear that signaling mechanisms of the luminal A, B and triple negative subtypes are different. Furthermore, the up- and down-regulated networks predicted subtype-specific drug targets and possible compensation circuits between up- and down-regulated genes. We believe these results may have significant clinical implications in the personalized treatment of cancer patients allowing an objective approach to the recycling of the arsenal of available drugs to the specific case of each breast cancer given their distinct qualitative and quantitative molecular traits.</p></div

(A) Gaussian distribution of differential expressed genes between BT-20 and MCF10A.

<p>(B) Correlation between <i>connectivity</i> and <i>betweenness centrality</i>. (C) Correlation between the connectivity of proteins considering the full network sample available (∼10,000) and sub-networks of ∼600 proteins in BT-474 breast cancer cell lines.</p

Mechanism of MT disassembly.

<p>(A) Radial energy distribution of the GDP-Model at the longitudinal inter-dimer interface is superposed on a protofilament to show how uneven the energy distribution is. This produces a torque that leads to outward curling of the protofilament. (B) Tangential energy distribution of the GDP-Model showing slight sideway tilting due to the slightly uneven distribution of energy. <i>α</i>-subunits are colored blue while <i>β</i>-subunits are red.</p

A matrix showing individual contributions of each subunit to longitudinal stability in the two simulated systems, in kcal/mol.

<p>A matrix showing individual contributions of each subunit to longitudinal stability in the two simulated systems, in kcal/mol.</p

Sub-network of differentially expressed genes between MDA-MB-231 (Triple-Negative) and MCF10A, represented in a circular layout.

<p>Nodes represent genes while links represent interaction between genes. Size nodes indicate connectivity and color represents an expression pattern between tumoral versus non-tumoral breast cell line. (A) p<0.05. (B) p<0.01. Gephi was used to present and visualize the networks.</p

Domain contributions to overall energy.

<p>Energetic contributions of important domains across lateral interface in (A) <i>α</i> and (B) <i>β</i> subunits and across longitudinal inter-dimer interface in (C) <i>α</i> and (D) <i>β</i> subunits. Data are shown for GDP- and GTP-Model as well as the difference between them (GTP-GDP). On the <i>x</i>-axis of (A) and (B), domains H4 helix and before occur at lateral interface of the ligand while domains after that occur at receptor lateral interface. In (C), all domains belong to receptor while all the domains in (D) belong to ligand. See ligand and receptor definitions in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004313#pcbi.1004313.g003" target="_blank">Fig 3</a>.</p

Detailed Per-residue Energetic Analysis Explains the Driving Force for Microtubule Disassembly

<div><p>Microtubules are long filamentous hollow cylinders whose surfaces form lattice structures of <i>αβ</i>-tubulin heterodimers. They perform multiple physiological roles in eukaryotic cells and are targets for therapeutic interventions. In our study, we carried out all-atom molecular dynamics simulations for arbitrarily long microtubules that have either GDP or GTP molecules in the E-site of β-tubulin. A detailed energy balance of the MM/GBSA inter-dimer interaction energy per residue contributing to the overall lateral and longitudinal structural stability was performed. The obtained results identified the key residues and tubulin domains according to their energetic contributions. They also identified the molecular forces that drive microtubule disassembly. At the tip of the plus end of the microtubule, the uneven distribution of longitudinal interaction energies within a protofilament generates a torque that bends tubulin outwardly with respect to the cylinder's axis causing disassembly. In the presence of GTP, this torque is opposed by lateral interactions that prevent outward curling, thus stabilizing the whole microtubule. Once GTP hydrolysis reaches the tip of the microtubule (lateral cap), lateral interactions become much weaker, allowing tubulin dimers to bend outwards, causing disassembly. The role of magnesium in the process of outward curling has also been demonstrated. This study also showed that the microtubule seam is the most energetically labile inter-dimer interface and could serve as a trigger point for disassembly. Based on a detailed balance of the energetic contributions per amino acid residue in the microtubule, numerous other analyses could be performed to give additional insights into the properties of microtubule dynamic instability.</p></div

Venn diagram including comparative analysis of the subtype-specific networks to predict subtype-specific therapy.

<p>Up-regulated genes (A) and down- regulated genes (B). Luminal A classification includes MCF-7, T47D e ZR751 cell lines; Luminal B, includes BT-474; and triple negative, BT-20, MDA-MB-231 and MDA-MB-468.</p

Energy diagrams of the complete MT ring.

<p>The diagram shows the magnitude of favorable interaction energies at each interface between two tubulin dimers, whether at (A) the lateral interface, or at (B) the longitudinal inter-dimer interface. The magnitude of the interactions is proportional to the swelling at each interface with swellings in (A) being exaggerated to aid viewing. Green lines represent GTP-Model while red lines represent GDP-Model.</p

Tubulin C-terminal tail states.

<p>(A) Up-state. C-terminal tails of α- and β-tubulin extended into the cytosol. (B) Down-state. C-terminal tails folded and interacting with the tubulin body. (C) Light grey sphere - α-tubulin tail down, Dark grey sphere - β-tubulin tail down, Blue sphere – β-tubulin tail up.</p