Editorial: Protein–Protein Interactions: Drug Discovery for the Future

Abstract

The human proteome is comprised of approximately 20,000 proteins and significantly more proteinprotein interactions (PPIs) that play pivotal roles in biological processes. Their dysregulation often results in the onset and progression of several diseases. PPIs therefore represent a treasure trove of disease modifying drug targets-However, targeting these is a challenging task when attempting to convert drug-like small molecules to therapeutics. When targeting PPIs, it is necessary to have a balance between the interacting proteins to provoke a therapeutic and not a significant adverse effect. This is elegantly illustrated by degradation of TP53 mediated by MDM2 which is prevented by Nutlin-3, in addition to other relevant PPIs which have been discovered (Gul and Hadian, 2014).A number of high quality articles relating to PPIs are reported in this Protein-Protein Interactions: Drug Discovery for the Future Research Topic that make use of a variety of experimental techniques. As mentioned above, the number of PPIs are vast, the methods proposed by Lawson et al. and Martino et al., allow the mapping and elucidating those PPIs that can be targeting by small molecules. This is particularly relevant in order to identify those PPIs which should be the focus of drug discovery efforts. For example, the TCL1 (T-Cell Leukemia/Lymphoma 1) oncogene and FHIT (Fragile Histidine Triad Diadenosine Triphosphatase) are relevant drug targets in cancer and their interacting partners have been studied extensively (Gaudio et al., 2012;Gaudio et al., 2013a;Gaudio et al., 2013b;Paduano et al., 2018). In many cases the interacting partners in PPIs are poorly understood and González-Avendaño et al. used proteomics approaches based on mass spectrometry to resolve complex interacting partners of well characterized proteins. Each novel PPI was further investigated with the aim of understanding its importance in the biology and biochemistry of cancer. It was also shown as PPIs are functional to signaling pathways that are up-regulated in cancer, such as PI3K-AKT, NFκB and drug resistance.Liang et al. used a drug discovery approach to consider not only drug activity and selectivity, but also drug-like properties and the associated primarily toxicity. To this end, the synthesis of new druglike small molecules or the design of close analogues, starting from natural products was undertaken in order to allow for suitable compound optimization. Chemical modifications, starting from the planar marine natural product fascaplysin, led to the identification of nonplanar tetrahydroβ-carboline analogs with conserved capability to bind selectively its target CDK4. The synthetically modified derivatives of the natural product resulted reduced toxicity using in vitro models.Parate et al. used a combination of the computational methods, biological and biophysical validations allow the realization of compelling studies of both drug-target and protein-protein interactions. By applying computational chemistry and Molecular Dynamics Simulations, the most relevant...

Application of In Silico Filtering and Isothermal Titration Calorimetry for the Discovery of Small Molecule Inhibitors of MDM2

“…Inhibition of the interaction of p53 with MDM2 exemplifies the growing interest in the therapeutic potential of modulating PPIs [50,51]. The estimated number of PPIs is ~10 6 .…”

Natural Product-Based Crop Protection Compounds─Origins and Future Prospects

“…RNAi technology is still of interest for weed management, but there appears to be much less development activity for this use. There are now a few pharmaceuticals that are small peptides (NPs) that prevent subunits of target site proteins from properly interacting (e.g., proper binding of protein subunits) . Similar to RNAi, particular small peptides may not occur in nature, but they are composed of natural compounds or may be modeled on a small, naturally occurring peptide neurohormone or toxin.…”

“…There are now a few pharmaceuticals that are small peptides (NPs) that prevent subunits of target site proteins from properly interacting (e.g., proper binding of protein subunits). 74 Similar to RNAi, particular small peptides may not occur in nature, but they are composed of natural compounds or may be modeled on a small, naturally occurring peptide neurohormone or toxin. Some pesticide companies are looking into use of small peptides that target pest protein−protein interactions in the same way as with pharmaceuticals.…”

Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea

“…Until now, few numbers have been reported of experimental PPIs between O. sativa and M. grisea that are inadequate to explore the pathogenic molecular mechanism ( Pellegrini et al., 1999 ; Jia et al. 2000 ; Krogh et al., 2001 ; Ng et al., 2003 ; Salwinski et al., 2004 ; Quevillon et al., 2005 ; Shoemaker and Panchenko, 2007 ; Wang et al., 2007 ; Najafabadi and Salavati, 2008 ; Parker et al., 2008 ; Ribot et al., 2008 ; Kumar and Nanduri, 2010 ; Mukhtar et al., 2011 ; Li et al., 2012 ; Maetschke et al., 2012 ; Mentlak et al., 2012 ; Park et al., 2012 ; Schleker et al., 2012 ; Simonsen et al., 2012 ; Meyer et al., 2013 ; Mosca et al., 2014 ; Rao et al., 2014 ; Sahu et al., 2014 ; Tully et al., 2014 ; Nourani et al., 2015 ; Li et al., 2016 ; Singh et al., 2016 ; Klopfenstein et al., 2018 ; Savojardo et al., 2018 ; Karan et al., 2019 ; Ma et al., 2019 ; Sahu et al., 2019 ; Loaiza et al., 2020 ; Lu et al., 2020 ; Singh et al., 2020 ; Wang et al., 2020 ; Rapposelli et al., 2021 ; Kumar et al., 2022 ; Mishra et al., 2022 ; Wu et al., 2015 ). Therefore, the computational approach is seen as an alternative method for the large-scale identification of PPIs.…”

“…; Huang et al, 2007;He et al, 2008;Jaeger et al, 2008;Najafabadi and Salavati, 2008;Parker et al, 2008;Ribot et al, 2008;Fones et al, 2010;Jain and Bader, 2010;Kumar and Nanduri, 2010;Mukhtar et al, 2011;Bai et al, 2012;Braun and Gingras, 2012;Das et al, 2012;Li et al, 2012;Maetschke et al, 2012;Mentlak et al, 2012;Park et al, 2012;Schleker et al, 2012;Cesari et al, 2013;Meyer et al, 2013;Devanna et al, 2014;Mosca et al, 2014;Rao et al, 2014;Sahu et al, 2014;Fujisaki et al, 2015;Huo et al, 2015;Nourani et al, 2015;Li et al, 2016;Berkey et al, 2017;Klopfenstein et al, 2018;Savojardo et al, 2018;Ding and Daisuke, 2019;El-Gebali et al, 2019;Karan et al, 2019;Ma et al, 2019;Sahu et al, 2019;Loaiza et al, 2020;Lu et al, 2020;Farooq et al, 2021;Rapposelli et al, 2021;Kumar et al, 2022;Mishra et al, 2022;…”