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LRRpredictor: a novel LRR motif detection method for irregular plant NLR protein motifs using a set of classifiers

By Eliza C. Martin 1 , Octavina C. A. Sukarta 2 , Laurentiu Spiridon 1 , Laurentiu G. Grigore 3 , Vlad Constantinescu 1 , Robi Tacutu 1 , Aska Goverse 2, * and Andrei-Jose Petrescu 1, *

Union Village Circle, Clifton, Va 20124

Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry of the Romanian Academy, Splaiul Independentei 296, 060031 Bucharest, Romania

Received: February 7, 2020 / Revised: February 28, 2020 / Accepted: March 4, 2020 / Published: March 8, 2020

Leucine-rich repeats (LRRs) belong to an archaic prokaryotic protein architecture that is highly involved in protein-protein interactions. In eukaryotes, LRR domains have evolved into key recognition modules in many classes of innate immune receptors. Due to the high sequence variability imposed by recognition specificity, precise repeat delineation is often difficult, especially in plant NOD-like receptors (NLRs), which are known to show much greater irregularity. To address this issue, we introduce here LRRpredictor, a method based on a set of estimators designed to better identify LRR motifs in general but especially adapted to handle more irregular LRR environments, thus allowing to compensate for the scarcity of structural data of NLR proteins. The extrapolation capability tested on a set of annotated LRR domains from six immune receptor classes shows the ability of LRRpredictor to retrieve all previously defined specific motif consensuses and extend LRR motif coverage over annotated LRR domains. This analysis confirms the greater variability of LRR motifs in plant and vertebrate NLRs compared to extracellular receptors, in agreement with previous studies. Therefore, LRRpredictor is able to provide novel insights into the diversification of LRR domains and robust support for structure-informed LRR analyzes in immune receptor function.

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Leucine-rich repeat prediction; supervised learning; LRR motive; LRR structure; NOD-like receptors; Leucine-rich repeat prediction of R proteins; supervised learning; LRR motive; LRR structure; NOD-like receptors; R proteins

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Leucine-rich repeat (LRR) domains are present in all branches of the tree of life. As they are involved in protein-protein interactions, LRR domains are found in receptors that have a large number of functions such as pathogen detection, immune response propagation, hormone perception, enzyme inhibition or cell adhesion [1]. In both plants and mammals, a number of studies have detailed adverse effects associated with mutations in LRR domains, as reported for several immune-related receptors, resulting in compromised functions and enhanced disease progression [2]. For example, mutation of a single residue in the LRR domain of the rice Pita receptor results in a complete loss of recognition against the fungus Magnaporthe grisea [3] while mutations in the metazoan NLRC4-LRR contribute to autoinflammatory disease phenotypes [4]. In addition, mutations in the kinase enzyme LRRK2 lead to Parkinson’s disease and other associated inflammatory diseases [5, 6], while mutations in leucine-rich proteoglycans are previously implicated in osteoarthritis [7] and last but not least , PRELP. Mutations may play a role in Hutchinson-Gilford, an accelerated progeroid syndrome characterized by premature aging [8]. Therefore, understanding the structural aspects of the binding properties and specificities of LRR domains opens wide possibilities for receptor engineering with broad implications not only for improving crop resistance to plant diseases, but also for a wide range of medical applications.

In innate immunity, LRR modules are found in multiple domain organizations in many classes of receptors, such as plant receptor-like kinases (RLKs), receptor-like proteins (RLPs), NOD-like receptors (NLRs) or metazoan NLRs, and Toll- like. receptors (TLRs). In basal plant immunity, LRR N-terminal domains face the extracellular environment and are found in receptor-like kinases (RLKs) or receptor-like proteins (RLPs) depending on the presence or absence of a cytosolic C-terminal kinase domain. receiver side. In contrast, LRRs constitute the C-terminal domains of intracellular NOD-like receptors (NLRs), also known as resistance (R) proteins, and face the cytosolic environment to mediate resistance against specific pathogens. Depending on their N-terminal domain, which is either a coiled-coil (CC) domain or a toll-like receptor (TIR) ​​domain, R proteins are divided into two main classes of NLRs: CNL and TNL receptors, respectively [ 9 ]. However, both of these classes contain a central nucleotide-binding domain (NBS) that acts as a “switch” that changes its conformation upon ADP/ATP binding [ 9 , 10 ]. Metazoan NLRs show a similar organization to plant NLRs. They encode a variety of N-terminal “sensors” (caspase activation and recruitment domains: CARD, baculovirus inhibitor of apoptosis repeat – BIR, etc.), the central STAND “switch” domain (signal transduction ATPases with numerous domains) – NBS/ NACHT domain (NAIP (neuronal apoptosis inhibitory protein), CIITA (MHC class II transcription activator), HET-E (Podospora anserina incompatibility locus protein) and TP1 (telomerase-associated protein)) and the LRR domain at the C-terminal end. Last but not least, we mention here metazoan toll-like receptors (TLRs) that have an extracellular LRR sensor domain as seen in the RLK/RLP case and a TIR domain on the cytosolic side involved in signal transduction [11] .

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Structurally, LRR domains have a 3D solenoidal “horseshoe” architecture composed of a variable number of repeats that each range from ≈15 to ≈30 amino acids in length. The repeats are held together through a network of hydrogen bonds that form a beta sheet located on the ventral side of the “horseshoe”. This is generated by a conserved sequence pattern called the LRR motif which in its minimal form is of the type “LxxLxL” where L is generally leucine and, to a lesser extent, other hydrophobic amino acids [12]. Comprehensive sequence analysis of LRR immune receptors resulted in several classifications of LRR domains showing preferred amino acid conservation outside the minimal motif, such as the two-type classification proposed by Matsushima et al. [13] for TLR receptors or the seven-type classification proposed by Kobe and Kajava [14] for all known LRR domains in all kingdoms. However, exceptions to such rules are frequent, as revealed by the hidden Markov model approach by Ng et al. [15]. This highlighted the fact that most of the analyzed human protein classes containing LRR domains also show many irregular motifs together with repeats that show the well-defined class-specific motif [ 15 ].

Although the aforementioned receptor classes have been shown to exhibit LRR irregularities [15], studies on plant NLR proteins such as Lr10 and Pm3 from wheat, Rx1 and Gpa2 from potato, or ZAR1 from Arabidopsis show that their LRR domains have a very more variable and variable. irregular structure than their extracellular counterparts [16, 17, 18, 19, 20, 21, 22]. These combined factors contribute to the challenge for accurate prediction of LRR motifs in plant NLRs.

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A proper annotation of each LRR motif in a given LRR domain is essential to generate an accurate 3D model [ 12 , 23 ] and thereby correctly define the surface of the domain and identify possible protein-protein interaction interfaces. An illustrative example is the conservation mapping carried out by Helft et al. in 2011, which was used to identify new interaction partners of plant RLPs and RLKs by studying conserved 3D relationships between amino acids inferred from the annotation of LRR repeats [ 24 ].

Based on our previous work, the identification of the true individual motifs in an LRR domain is hindered by the following: (a) in its minimal form, an “LxxLxL” pattern is trivial and often occurs randomly in any protein; (b) in many cases, multiple ‘LxxLxL’ patterns overlap within a range of less than 15 aa in NLR-LRR making precise delineation difficult; (c) the number of 3D experimental structures to learn from is low; and (d) this small 3D learning set is biased by class and phyla, as about half of the structures are of mammalian origin while plant NLRs have only one recently documented structure [ 21 , 22 ].

So, given

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