**Preface to "Recent Developments on Protein–Ligand Interactions: From Structure, Function to Applications"**

Protein–ligand interactions play a fundamental role in most major biological functions. The number and diversity of small molecules that interact with proteins, whether naturally or not, can quickly become overwhelming. They are as essential as amino acids, nucleic acids or membrane lipids, enabling a large number of essential functions. One need only think of carbohydrates or even just ATP to be certain. They are also essential in drug discovery. With the increasing structural information of proteins and protein–ligand complexes, molecular modelling, molecular dynamics, and chemoinformatics approaches are often required for the efficient analysis of a large number of such complexes and to provide insights. Similarly, numerous computational approaches have been developed to characterize and use the knowledge of such interactions, which can lead to drug candidates. For instance, one main application is to identify tractable chemical startpoints that non-covalently modulate the activity of a biological molecule. This new information brings questions that affect chemistry, biology, and even poses specific computer problems.

"Recent Developments on Protein–Ligand Interactions: From Structure, Function to Applications" was dedicated to the different aspect of protein–ligand analysis and/or prediction using computational approaches, as well as new developments dedicated to these tasks.

The 15 published papers clearly show the extent of such a focus, ranging from general analyses on a large dataset, to specific methodological developments, to applications of biomedical interest. It will interest both specialists and non-specialists, as the presented studies cover a very large spectrum in terms of methodologies and applications. It underlines the variety of scientific area linked to these questions, i.e., chemistry, biology, physics, informatics, bioinformatics, structural bioinformatics and chemoinformatics. I would like to use this editorial to thank all the researchers who submitted papers for this Special Issue and made it a success with work of great interest.

In the context of applications dedicated to a specific system, Aguero and Terreux were working on an explosive subject, 1,3,5,7-tetranitro-1,3,5,7-tetrazocane (HMX), an explosive that pollutes many sites. In order to decontaminate these sites, bioremediation was a promising approach. They therefore set out to improve a nitroreductase from Enterobacter Cloacaetowards HMX. With the Coupled Moves algorithm from Rosetta, they redesigned the active site around HMX, and analysed the results with Molecular Dynamics, showing encouraging results [1].

Bienboire-Frosini and co-workers were looking at cat allergies. The major cat allergen Fel d1 was a tetrameric glycoprotein of the secretoglobin superfamily, but its biological function was uncertain. They therefore used bioinformatics approaches to search for potential ligands and then experimentally tested them. The specific affinity of Fel d1 to semiochemicals supports a function of the protein in cat chemical communication, and pointed to a putative role for secretoglobins in protein semiochemistry [2].

Dharmatti and colleagues focussed on folate receptor (FR), a major target for cancer treatment and detection. They tried to enhance by click-reaction FR binding affinity by peptide conjugation. After multiple optimisations, the designed peptides resulted in an increase in the number of interaction sites, leading to potentially interesting drug developments [3].

Fan and co-workers worked on pimaricin, a polyene antibiotic of great pharmaceutical

significance. Using Molecular Dynamics, they compared different stages of the molecules and showed how pimaricin thioesterase-catalyzed macrocyclization evolved, as the protein-polyketide recognition, and product release; they underlined potential residues for rational modification of pimaricin thioesterase [4].

Kim and colleagues have looked at monoclonal antibodies (mAbs) potentially interesting in cancer immunotherapy. mAb-based drugs have some drawbacks, e.g., poor tumour penetration. BMS-8, one of the potent small molecule drugs inhibits PD-1. Using in silico simulation, they optimized and successfully tested a derived compound five times more affine [5].

Nshogoza and co-workers analysed TDP-43, as an RNA-binding protein, implicated in neurodegenerative and cancer diseases. By combining Nuclear Magnetic Resonance and in silico approaches such as HADDOCK, they designed, tested and provided explanations of their binding to RNA Recognition Motifs of TDP-43 [6].

Pal and colleagues worked on galectins, a family of galactoside-recognizing proteins involved in different galectin-subtype-specific inflammatory and tumour-promoting processes. They synthetized and assessed interest of 3-C-methyl-gulopyranoside derivatives as galectin inhibitors with good affinity and selectivity [7].

Potthoff et al. applied molecular modelling approaches to build a structural model of full-length procollagen C-proteinase enhancer-1 (PCPE-1), which was not experimentally available. They characterized the interactions between the extracellular matrix PCPE-1 protein, and glycosaminoglycans (GAGs). They predicted GAG binding poses for various GAG lengths, types and sulfation pattern [8].

Reyes-Espinosa et al. focussed on an issue of economic importance, namely pesticide resistance. To do so, they modelled the complex-ligands of nine acetylcholinesterase structures of Lepidopteran organisms and 43 organophosphorus pesticides. The analysis of the complexes allowed a better understanding of the specificities of the variants of each species [9].

The enzyme phospholipase C gamma 1 (PLC1) was a potential drug target of interest for various pathological conditions such as immune disorders, systemic lupus erythematosus, and cancers. Tripathi and colleagues targeted its SH3 domain and various binding partners. They identified with molecular dynamic simulation the critical interacting essential residues leading to the possibility to also identify new inhibitors [10].

Caspases not only contributed to the neurodegeneration associated with Alzheimer's disease, but also played essential roles in promoting the underlying pathology of this disease. Xue et al. applied the Movable Type free energy method, a Monte Carlo sampling method extrapolating the binding free energy by simulating the partition functions for both free-state and bound-state protein and ligand configurations, to the caspase-inhibitor binding affinity study. They tested more than a hundred active inhibitors binding to caspase-3 on one side, and smaller well-characterized datasets on the other side. These studies revealed how small structural changes affected the caspase-inhibitor interaction energies [11].

Major difficulties for comparing docking predictions with experiments mostly came from the lack of transferability of experimental data and the lack of standardisation in molecule names. Gheyouche and colleagues have conceived the DockNmine platform to provide a service allowing an expert and authenticated annotation of ligands and targets. Researcher incorporated controlled information in the database using reference identifiers for the protein and the ligand, the data and the publication associated to it. It allowed the incorporation of docking experiments using forms that automatically parse useful parameters and results. Pre-computed outputs to assess the degree of correlations between docking experiments and experimental data were also provided [12].

Polishchuk and co-workers analysed the fact that pharmacophores derived from molecular dynamics simulations were more relevant than those just taken from rigid structures, but also generated a strong redundancy. They therefore proposed an approach to limit the number of pharmacophores, and showed its relevance [13].

Conserved three-dimensional (3D) patterns among protein structures provided valuable insights into protein classification, functional annotations or the rational design of multi-target drugs. Valdes-Jim ´ enez and colleagues developed 3D-PP, a new free access web server for the discovery and ´ recognition all similar 3D amino acid patterns among a set of protein structures independent of their sequence similarity. This new tool did not require any previous structural knowledge about ligands, and all data were organized in a high-performance graph database [14].

Finally, the number of available protein structures in the Protein Data Bank (PDB) had considerably increased in recent years. Here, with Nicolas Shinada and Peter Schmidtke, we presented a specific clustering of protein-ligand structures to avoid bias found in different studies. The methodology was based on binding site superposition, and a combination of weighted Root Mean Square Deviation assessment and hierarchical clustering. Defining these categories decreased by 3.84-fold the number of complexes, and offered more refined results compared to a protein sequence-based method [15].

#### **References**

[1] S. Aguero, R. Terreux, Degradation of High Energy Materials Using Biological Reduction: A Rational Way to Reach Bioremediation, International Journal of Molecular Sciences 20(22) (2019) 5556.

[2] C. Bienboire-Frosini, R. Durairaj, P. Pelosi, P. Pageat, The Major Cat Allergen Fel d 1 Binds Steroid and Fatty Acid Semiochemicals: A Combined In Silico and In Vitro Study, International Journal of Molecular Sciences 21(4) (2020) 1365.

[3] R. Dharmatti, H. Miyatake, A. Nandakumar, M. Ueda, K. Kobayashi, D. Kiga, M. Yamamura, Y. Ito, Enhancement of Binding Affinity of Folate to Its Receptor by Peptide Conjugation, International Journal of Molecular Sciences 20(9) (2019) 2152.

[4] S. Fan, R. Wang, C. Li, L. Bai, Y.-L. Zhao, T. Shi, Insight into Structural Characteristics of Protein-Substrate Interaction in Pimaricin Thioesterase, International Journal of Molecular Sciences 20(4) (2019) 877 .

[5] E.-H. Kim, M. Kawamoto, R. Dharmatti, E. Kobatake, Y. Ito, H. Miyatake, Preparation of Biphenyl-Conjugated Bromotyrosine for Inhibition of PD-1/PD-L1 Immune Checkpoint Interactions, International Journal of Molecular Sciences 21(10) (2020) 3639.

[6] G. Nshogoza, Y. Liu, J. Gao, M. Liu, S.A. Moududee, R. Ma, F. Li, J. Zhang, J. Wu, Y. Shi, K. Ruan, NMR Fragment-Based Screening against Tandem RNA Recognition Motifs of TDP-43, International Journal of Molecular Sciences 20(13) (2019) 3230.

[7] K.B. Pal, M. Mahanti, H. Leffler, U.J. Nilsson, A Galactoside-Binding Protein Tricked into Binding Unnatural Pyranose Derivatives: 3-Deoxy-3-Methyl-Gulosides Selectively Inhibit Galectin-1, International Journal of Molecular Sciences 20(15) (2019) 3786.

[8] J. Potthoff, K.K. Bojarski, G. Kohut, A.G. Lipska, A. Liwo, E. Kessler, S. Ricard-Blum, S.A. Samsonov, Analysis of Procollagen C-Proteinase Enhancer-1/Glycosaminoglycan Binding Sites and of the Potential Role of Calcium Ions in the Interaction, International Journal of Molecular Sciences 20(20) (2019) 5021.

[9] F. Reyes-Espinosa, D. Mendez- ´ Alvarez, M.A. P ´ erez-Rodr ´ ´ıguez, V. Herrera-Mayorga, A. Juarez-Saldivar, M.A. Cruz-Hern ´ andez, G. Rivera, In Silico Study of the Resistance ´ to Organophosphorus Pesticides Associated with Point Mutations in Acetylcholinesterase of Lepidoptera: *B. mandarina, B. mori, C. auricilius, C. suppressalis, C. pomonella, H. arm´ıgera, P. xylostella, S. frugiperda*, and *S. litura*, International Journal of Molecular Sciences 20(10) (2019) 2404.

[10] N. Tripathi, I. Vetrivel, S. Teletch ´ ea, M. Jean, P. Legembre, A.D. Laurent, Investigation of ´ Phospholipase C1 Interaction with SLP76 Using Molecular Modeling Methods for Identifying Novel Inhibitors, International Journal of Molecular Sciences 20(19) (2019) 4721.

[11] S. Xue, H. Liu, Z. Zheng, Application of the Movable Type Free Energy Method to the Caspase-Inhibitor Binding Affinity Study, International Journal of Molecular Sciences 20(19) (2019) 4850.

[12] E. Gheyouche, R. Launay, J. Lethiec, A. Labeeuw, C. Roze, A. Amosse, S. T ´ eletch ´ ea, ´ DockNmine, a Web Portal to Assemble and Analyse Virtual and Experimental Interaction Data, International Journal of Molecular Sciences 20(20) (2019) 5062.

[13] P. Polishchuk, A. Kutlushina, D. Bashirova, O. Mokshyna, T. Madzhidov, Virtual Screening Using Pharmacophore Models Retrieved from Molecular Dynamic Simulations, International Journal of Molecular Sciences 20(23) (2019) 5834.

[14] A. Valdes-Jim ´ enez, J.-L. Larriba-Pey, G. N ´ u´nez-Vivanco, M. Reyes-Parada, 3D-PP: A Tool ˜ for Discovering Conserved Three-Dimensional Protein Patterns, International Journal of Molecular Sciences 20(13) (2019) 3174.

[15] N.K. Shinada, P. Schmidtke, A.G. de Brevern, Accurate Representation of Protein-Ligand Structural Diversity in the Protein Data Bank (PDB), International Journal of Molecular Sciences 21(6) (2020) 2243.

> **Alexandre G. de Brevern** *Editor*
