Legumes are seed plants belonging to the Leguminosae family, spanning more than 13000 species across 600 genera. Legumes are diverse; they are suitable for cultivation in a variety of environments and temperatures (Magrini et al., 2019). They are an excellent source of antioxidants, micronutrients, and proteins, finding use as natural fertilizers, medicines, and animal fodder (Shah et al., 2020; Singh et al., 2022). Apart from their health benefits, legumes naturally have the ability to fix nitrogen from the atmosphere symbiotically, which has positive effects on agriculture and soil enrichment that are both long-lasting and economically viable. Improvement in legume cultivation and their increased usage can help ensure food security and contribute to better soil fertility (Foyer et al., 2016; Singh et al., 2020).

However, one of the main impediments in the way of abundant and quality yield of legume crops is pests and diseases Figure 1 . Furthermore, climate changes have made it easier for various species to move freely, which has led to the emergence of new diseases that could grow into uncontrollable epidemics and endanger food security (Piquerez et al., 2014). Thus, crop improvement is a key component of sustainable agriculture and can be accomplished using a variety of techniques, ranging from traditional breeding to genetic engineering. To increase the effectiveness of the breeding process, it is necessary to have a thorough understanding of host-pathogen interactions as well as effective resistance mechanisms at the cellular, genetic, and molecular levels. The most effective, affordable, and environmentally friendly of the various control techniques available is breeding for resistance (Rubiales et al., 2014). These factors drive plant breeders and scientists to focus their efforts on finding mechanisms for plant disease resistance (Osuna-Cruz et al., 2018). Indeed, studies into the genes and genomes of legumes have provided valuable insight into disease resistance components and other agronomic traits (Leal-Bertioli et al., 2009; Khera et al., 2018; Zhuang et al., 2019; Sampaio et al., 2020).

Resistance genes (R genes) are variable plant genes that confer resistance to vast varieties of biotrophic pathogens, including viruses. As they can specifically recognise the matching pathogen effectors or associated protein(s), resistance (R) genes are the most potent defences against pathogen invasion (Liu et al., 2007). This allows plants to mount an effective defence at the site of infection. R genes are capable of expressing PR proteins in response to physical or chemical stimuli (Rodríguez-Sifuentes et al., 2020). As PR proteins have hydrolytic activity, contact toxicity, involvement in defence signalling, and pathogen enzyme inhibition, they constitute a variety of weapons against pathogens. We have seen that plant R genes have been divided into several groups based on their typical domains (Zheng et al., 2016). A class of proteins containing nucleotide binding (NB) and leucine-rich repeat (LRR) domains is encoded by the majority of R genes (Friedman and Baker, 2007).

It is now known that R genes also produce proteins with a series of carboxy-terminal leucine-rich repeats (LRRs), a putative amino-terminal signalling domain, and a nucleotide binding site (NBS). There are two primary classes of “NBS-LRR” proteins: first which encodes an amino-terminal coiled-coiled motif (CC-NBS-LRR or CNL proteins) and second which has an amino-terminal TIR (Toll/interleukin receptor) domain (which are known as TIR-NBS-LRR or TNL proteins) (Meyers et al., 2005). Figure 2 depicts the various classes of R-Gene. Hence, the participation of multiple R genes, post-transcriptional regulators, and biotic and abiotic stressors limits the ability of plants to resist disease.

R gene mechanisms work on the gene-for-gene concept (Kaloshian, 2004). The resistance occurs only when the R gene proteins engage with the pathogen Avr gene in a certain form. They can interact with the pathogen gene in two ways, first by directly interacting with its protein product and second, if it plays a catalytic role by interacting with something created by the Avr gene product (Hulbert et al., 2001). Any attempt of an infection is thus governed by both the genotype of the host and that of the pathogen. Once this recognition has taken place, the defence reactions are triggered. Hypersensitive reaction is frequently characterized in these defence reactions, which results in the death of the initial cell or cells infected and also the local accumulation of antimicrobial compounds (Moffett, 2009). Phytophthora root and stem rot in nonhost common bean has been found to induce a strong hypersensitive response to Phytophthora sojae due to upregulation of genes promoting phaseollidin and glyceollin production, which have significant inhibitory effects on oospore production and mycelial formation (Bi et al., 2022). Transcription factors have also been implicated in multilayer defence signalling against Fusarium wilt disease in chickpea (Chakraborty et al., 2020). Legumes exhibit resistance through other means as well; a recent study on Faba beans indicated that its resistant genotype withstood the Chocolate spot infection caused by Botrytis fabae due to a better Photosystem II Repair mechanism at early stages of the infection (Castillejo et al., 2021). Leaf spot infected alfalfa, when colonized by arbuscular mycorrhizal fungus (AMF), has shown to ameliorate the effects of the infection, thus displaying the potential to serve as a biocontrol strategy for leaf spot infection of alfalfa (Li et al., 2019).

Development of cost-effective next generation sequencing platforms with enhanced performances have given way to copious amounts of data. In order for these data to be effectively analysed and compared, various data management practices are being employed (Bauchet et al., 2019). Many databases exist for comprehensive study and management of the insurmountable data generated by plant genome studies. However, these repositories either only focus individually on model genomes such as soybean (https://soybase.org/) (Grant et al., 2009), Medicago truncatula (https://www.legoo.org) (Carrere et al., 2019), (http://www.medicago.org/MtDB) (Lamblin, 2003), or a combination of multiple model species (http://www.plantgdb.org/) (Dong, 2004), (http://plantgrn.noble.org/LegumeIP/) (Li et al., 2011). Currently, there is a lack of repositories that can pool the numerous disease resistance genes with proteomics and facilitate the quick integration of crucial resistance genes into breeding methods. Keeping this in mind, we have established and developed the first comprehensive database of disease resistance genes in legume plants named as LDRGDb, that incorporates knowledge about resistant genes, QTLs, and their loci, with proteomics, pathways interactions as well as structural conformations of proteins. The database serves as a medium for comprehensive search and retrieval of relevant information, and will help researchers understand various biological phenomena with relative ease. Our database has been constructed so as to aid in research of disease resistant common legume cultivars and their underlying mechanisms. LDRGDb spans 10 legume species, with genes, QTL information such as linkage group, neighbouring marker, population, maternal and paternal parent, proteomics, informatics such as molecular weight, theoretical pI, length of amino acids, half-life of protein, and aliphatic index with emphasis on structural conformation, which can aid in a thorough understanding of various pathways and interactions involved in disease resistance, functioning as a single repository for assisting in research.

Disease-resistant mechanisms of plants are constrained by the involvement of various genes, post-transcriptional controls, biotic and abiotic stresses. The understanding of these mechanisms and their impact on plant proteomes and metabolomes is important for improvement of legume production. To obtain knowledge about the mechanisms of resistance, LDRGDb serves as an exemplary one stop database for quick access of disease resistance genes, QTLs, and the proteins and pathways associated with them for various legume species. Users can search using either disease names, crop names, or both, facilitating easy and comprehensive ingress into the data. The data collected from various databases and literature has been meticulously structured to allow for rapid searches for diseases, legumes and the genes associated with them using user-friendly web interfaces. The database has specific sections for particular crop characteristics, prevalent diseases, as well as a FAQ section for frequently asked questions. These are the webpage screenshots ( Figure 7 ).

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

HS contributed to collection of data implementation and initial draft writing. AiK contributed to collection of data, initial draft writing tables, and figures. AA contributed to collection of data and initial draft writing. AK contributed to designing and editing the manuscript. NS conceived, supervised and edited the final manuscript. CJ conceived, supervised and edited the final manuscript. All authors contributed to the article and approved the submitted version.