Neuropeptide Y Peptide Family and Cancer: Antitumor Therapeutic Strategies

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Abstract

Currently available data on the involvement of neuropeptide Y (NPY), peptide YY (PYY), and pancreatic polypeptide (PP) and their receptors (YRs) in cancer are updated. The structure and dynamics of YRs and their intracellular signaling pathways are also studied. The roles played by these peptides in 22 different cancer types are reviewed (e.g., breast cancer, colorectal cancer, Ewing sarcoma, liver cancer, melanoma, neuroblastoma, pancreatic cancer, pheochromocytoma, and prostate cancer). YRs could be used as cancer diagnostic markers and therapeutic targets. A high Y1R expression has been correlated with lymph node metastasis, advanced stages, and perineural invasion; an increased Y5R expression with survival and tumor growth; and a high serum NPY level with relapse, metastasis, and poor survival. YRs mediate tumor cell proliferation, migration, invasion, metastasis, and angiogenesis; YR antagonists block the previous actions and promote the death of cancer cells. NPY favors tumor cell growth, migration, and metastasis and promotes angiogenesis in some tumors (e.g., breast cancer, colorectal cancer, neuroblastoma, pancreatic cancer), whereas in others it exerts an antitumor effect (e.g., cholangiocarcinoma, Ewing sarcoma, liver cancer). PYY or its fragments block tumor cell growth, migration, and invasion in breast, colorectal, esophageal, liver, pancreatic, and prostate cancer. Current data show the peptidergic system’s high potential for cancer diagnosis, treatment, and support using Y2R/Y5R antagonists and NPY or PYY agonists as promising antitumor therapeutic strategies. Some important research lines to be developed in the future will also be suggested.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures

David Sehnal, Sebastian Bittrich, Mandar Deshpande … (2021)

Large biomolecular structures are being determined experimentally on a daily basis using established techniques such as crystallography and electron microscopy. In addition, emerging integrative or hybrid methods (I/HM) are producing structural models of huge macromolecular machines and assemblies, sometimes containing 100s of millions of non-hydrogen atoms. The performance requirements for visualization and analysis tools delivering these data are increasing rapidly. Significant progress in developing online, web-native three-dimensional (3D) visualization tools was previously accomplished with the introduction of the LiteMol suite and NGL Viewers. Thereafter, Mol* development was jointly initiated by PDBe and RCSB PDB to combine and build on the strengths of LiteMol (developed by PDBe) and NGL (developed by RCSB PDB). The web-native Mol* Viewer enables 3D visualization and streaming of macromolecular coordinate and experimental data, together with capabilities for displaying structure quality, functional, or biological context annotations. High-performance graphics and data management allows users to simultaneously visualise up to hundreds of (superimposed) protein structures, stream molecular dynamics simulation trajectories, render cell-level models, or display huge I/HM structures. It is the primary 3D structure viewer used by PDBe and RCSB PDB. It can be easily integrated into third-party services. Mol* Viewer is open source and freely available at https://molstar.org/ . Graphical Abstract Overview of the large array of entities and systems that can be visualized and be manipulated with by the Mol* Viewer.