A computational approach to identify the role of potassium transporter in barley

Abstract

Potassium ion $\left(K^{+}\right)$ is an essential macronutrient for plant growth and survival. $K^{+}$ involves several biochemical and physiological pathways and hence its regulation is essential for normal metabolism and cellular functions. $K^{+}$ homeostasis is maintained by many proteins in a coordinated manner. In barley at least fifteen proteins are directly involved in potassium transport. Interaction among these proteins forms a network along with forty-nine other proteins. We retrieved the potassium transporter interaction network from STRING and the network was analyzed for centrality measures using Cytoscape. Based on the centrality measures such as betweenness, bridging, closeness, degree, eccentricity, eigenvector, radiality and stress, the proteins MLOC_6793.2, MLOC_14891.1, MLOC_16944.1, MLOC_57408.2, and MLOC_63991.1 are found to be most important for the network. These five proteins interact among themselves and form a subgraph. MLOC_63991.1 is the most essential protein as it links other 4 proteins in the network. MLOC_63991.1 is a monomer with 769 amino acid sequences. This protein is predicted and described as potassium transporter.

Keywords:

• Potassium
• Centrality
• Barley
• Protein–protein interaction network

1 Introduction

Potassium $\left(K^{+}\right)$ is one of the vital plant nutrients. It is essential for maintaining electrical potential, hydrostatic pressure and biochemical activity for many enzymes [1]. $K^{+}$ is transported by non-specific ion channels and high affinity transporter present in plasma membrane in the root system [2]. These protein channels play a crucial role in maintaining ion homeostasis inside the cell [3]. $K^{+}$ is transported by active transport against concentration gradient [4].

A number of abiotic stresses affect the intercellular balance of $K^{+}$ and leads to the impairment in plant growth and reproduction. In barley (Hordeum vulgare) the existence of ion channels was stated by many researchers [5,6]. The stress tolerant plant accessions try to maintain the normal and even high concentration of $K^{+}$ during stressed condition. The background of the transport mechanism involved in the uptake and maintenance of higher concentration of $K^{+}$ in plants has an agricultural significance [5]. In this paper a computational approach is used to identify the role of potassium transporter in barley plant. We assume that the ion transporters present in barley forms an interaction network among them in a coordinated manner to regulate the uptake of $K^{+}$.

Materials and method

Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) is a database meant for predicting the protein–protein interactions of known and predicted proteins based on experimental data, computational estimate methods and public text collections [7]. A total of fifteen potassium transporters were identified till date in barley as mentioned in PFAM and INTERPRO protein domains. Potassium transporter expressed in barley plants were MLOC_10758.2, MLOC_11567.1, MLOC_11780.6, MLOC_16004.3, MLOC_16185.1, MLOC_30826.1, MLOC_37174.3, MLOC_53246.1, MLOC_58287.2, MLOC_62643.1, MLOC_63991.1, MLOC_66784.2, MLOC_70288.1, MLOC_71693.1, and MLOC_74565.1 (Table 1). All the fifteen proteins were analyzed by STRING [6].

Table 1 Potassium transporters predicted in Hordium vulgare as per STRING data base.

Protein ID	Protein annotation
MLOC_10758.2	Potassium transporter; Potassium transporter (784 aa)
MLOC_11567.1	Predicted protein; Potassium transporter (800 aa)
MLOC_11780.6	Predicted protein; Potassium transporter (720 aa)
MLOC_16004.3	Predicted protein; Potassium transporter (621 aa)
MLOC_16185.1	HAK2 ; Potassium transporter (777 aa)
MLOC_30826.1	Predicted protein; Potassium transporter (565 aa)
MLOC_37174.3	Predicted protein; Potassium transporter (625 aa)
MLOC_53246.1	Potassium transporter; Potassium transporter (614 aa)
MLOC_58287.2	HAK3; Potassium transporter (866 aa)
MLOC_62643.1	Predicted protein; Potassium transporter (646 aa)
MLOC_63991.1	Predicted protein; Potassium transporter (769 aa)
MLOC_66784.2	Predicted protein; Potassium transporter (792 aa)
MLOC_70288.1	Predicted protein; Potassium transporter (743 aa)
MLOC_71693.1	Putative high-affinity potassium transporter;
	Potassium transporter (775 aa)
MLOC_74565.1	Predicted protein; Potassium transporter (780 aa)

Legend: aa—amino acid.

These proteins have no direct interaction with each other. In order to find the relationship between these proteins, forty-nine 1st shell proteins were added. A total of sixty-four proteins formed a highly complex protein–protein interaction (PPI) network with 448 edges. The interaction may be physical or functional. These databases provide functional enhancements analysis report. The functional classification systems use in house prediction along with COG, Ensembl, Intact, RefSeq, PubMed, Reactome, DIP, BioGRID, MINT, KEGG, SGD, FlyBase, SwissProt/UniProt, SwissModel, HUGO, OMIM, NCI/Nature PID, PDB, The Interactive Fly, BioCyc, Gene Ontology, and SIMAP [6]. This PPI network is highly useful to identify the cellular processes involved in potassium transport at system-level. Interaction networks can also suggest new directions for the experimental studies.

PPI network formed by 63 proteins and with 448 interactions were further analyzed using graph theory based Centrality measures. The software Cytoscape (v 3.6.1) helps us to visualize molecular interactions and to analyze networks of any kind. Its plugins help to analyze molecular profiles.

Result

The fifteen potassium transporter proteins had no direct or indirect interaction among them. These proteins interact with 49 other proteins and form an interaction network with the medium confidence level (0.4) for minimum required interaction score (Table 2)(Fig. 1).

Fig. 1 Protein–protein interaction network formed by fifteen potassium transporters and its forty nine first shell interaction proteins.

Table 2 Predicted functional partners for the Potassium transporter with medium level of confidence with first shell interaction based on gene neighborhood, text mining and experimentally determined scores.

Protein ID	Protein	Gene	Experimentally	Text	Total
	annotation	neighborhood	determined	mining	score
MLOC_34636.1	Uncharacterized protein (299 aa)		$•$		0.573
MLOC_10278.1	Predicted protein (307 aa)		$•$		0.573
MLOC_3013.1	Predicted protein (357 aa)		$•$		0.562
MLOC_22271.1	Predicted protein (366 aa)		$•$		0.562
MLOC_22145.2	Predicted protein (361 aa)		$•$		0.562
MLOC_68594.1	Uncharacterized protein (532 aa)	$\circ$		$•$	0.528
MLOC_61362.2	Non-specific serine/threonine protein kinase (461 aa)		$\circ$	$•$	0.499
MLOC_74879.2	Predicted protein (898 aa)			$•$	0.497
MLOC_57408.2	Predicted protein (1107 aa)	$\circ$		$•$	0.486
MLOC_16944.1	Predicted protein (535 aa)	$\circ$		$•$	0.486
MLOC_57545.1	Uncharacterized protein (392 aa)	$\circ$		$•$	0.445
MLOC_37763.1	Predicted protein (730 aa)	$\circ$		$•$	0.445
MLOC_81219.2	Predicted protein (1045 aa)		$\circ$	$•$	0.444
MLOC_69266.3	Uncharacterized protein (902 aa)		$\circ$	$•$	0.428
RGA1	NBS-LRR resistance protein analog 1; RGA1 (895 aa)		$\circ$	$•$	0.428
MLOC_5059.1	Predicted protein (895 aa)		$\circ$	$•$	0.428
MLOC_20651.1	Uncharacterized protein (647 aa)		$\circ$	$•$	0.428
MLOC_7783.1	Predicted protein (256 aa)			$•$	0.422
MLOC_64392.1	Predicted protein (340 aa)			$•$	0.422
MLOC_62717.1	Uncharacterized protein (384 aa)			$•$	0.422
MLOC_62700.1	Uncharacterized protein (469 aa)			$•$	0.422
MLOC_60871.1	Uncharacterized protein (272 aa)			$•$	0.422
MLOC_57751.1	Predicted protein (108 aa)			$•$	0.422
MLOC_5129.1	Predicted protein (123 aa)			$•$	0.422
MLOC_51219.1	Predicted protein (620 aa)			$•$	0.422
MLOC_38597.1	Uncharacterized protein (388 aa)			$•$	0.422
MLOC_37297.1	Uncharacterized protein (385 aa)			$•$	0.422
MLOC_36509.1	Uncharacterized protein (692 aa)			$•$	0.422
MLOC_36424.1	Uncharacterized protein (989 aa)			$•$	0.422
MLOC_22943.1	Predicted protein (89 aa)			$•$	0.422
MLOC_22108.1	Uncharacterized protein (269 aa)			$•$	0.422
MLOC_218.1	Predicted protein (111 aa)			$•$	0.422
MLOC_14178.1	Uncharacterized protein (262 aa)			$•$	0.422
MLOC_4602.3	Sodium/hydrogen exchanger (538 aa)			$•$	0.422
MLOC_10701.1	Sodium/hydrogen exchanger (546 aa)			$•$	0.422
MLOC_80515.1	Predicted protein (1051 aa)		$\circ$	$•$	0.421
MLOC_56473.2	Uncharacterized protein (1052 aa)		$\circ$	$•$	0.421
MLOC_20132.1	Uncharacterized protein (1048 aa)		$\circ$	$•$	0.421
MLOC_16613.8	Predicted protein (1055 aa)		$\circ$	$•$	0.421
MLOC_10527.1	Uncharacterized protein (1003 aa)		$\circ$	$•$	0.421
MLOC_3823.2	Predicted protein (187 aa)			$•$	0.420
MLOC_31529.1	Predicted protein (94 aa)			$•$	0.420
MLOC_61455.1	Predicted protein (290 aa)			$•$	0.410
MLOC_73066.1	Predicted protein (902 aa)		$\circ$	$•$	0.404
MLOC_62861.3	Uncharacterized protein (566 aa)	$\circ$		$•$	0.403
MLOC_6793.2	Predicted protein (482 aa)			$•$	0.402
MLOC_62057.1	Uncharacterized protein (291 aa)			$•$	0.402
MLOC_14891.1	Predicted protein (477 aa)			$•$	0.402
MLOC_81045.1	Predicted protein (234 aa)			$•$	0.400

Legends: $\circ$ represents low confidence(0.15); $•$ represents medium confidence(0.4) for the interaction score.

The interactions are predicted from curated databases, experiments, gene neighbor-hood, and text mining. The interactions may also be predicted based on co-expression, and protein homology. For a similar kind of network the expected number of edges was 197. However, the actual number of edges is 448, which is much higher. An average node degree was 14.2, average local clustering coefficient was 0.622 and PPI enrichment $p$-value was less than $1.0\times 10^{-16}$. The network is given in Fig. 1.

PFAM protein domains for potassium transporters and its associated protein showed that 10 domains were significantly enriched by 36 proteins (Table 3). Function and domain for 27 proteins were not known. Table 4 represents the INTERPRO protein domains and features for potassium transporters and its associated protein. It showed that 25 domains were significantly enriched by 43 proteins and for 20 proteins function and domain are not known. It also predicts that 11 proteins are involved in more than one domain. Table 5 represents the average, minimum, and maximum score for the protein–protein interaction network analysis by STRING.

Table 3 PFAM protein domains for potassium transporters and its associated protein.

Pathway	Pathway	Observed	False	Matching proteins
ID	description	gene	discovery	in the network
		count	rate	(IDs and labels)
PF02705	$K^{+}$ potassium	14	$3.66\times 10^{-28}$	MLOC_10758.2,MLOC_11567.1,
	transporter			MLOC_11780.6,MLOC_16004.3,
				MLOC_30826.1,MLOC_37174.3,
				MLOC_53246.1,MLOC_58287.2,
				MLOC_62643.1,MLOC_63991.1,
				MLOC_66784.2,MLOC_70288.1,
				MLOC_71693.1,MLOC_74565.1
PF00999	Sodium/ hydrogen	4	0.0000767	MLOC_10701.1,MLOC_16944.1,
	exchanger family			MLOC_4602.3,MLOC_57408.2
PF08263	Leucine rich	8	0.0000767	MLOC_10527.1,MLOC_16613.8,
	repeat N-terminal			MLOC_20132.1,MLOC_56473.2,
	domain			MLOC_64392.1,MLOC_73066.1,
				MLOC_80515.1,MLOC_81219.2
PF00215	Orotidine	2	0.00441	MLOC_14891.1,MLOC_6793.2
	5-phosphate
	decarboxylase/
	HUMPS family
PF02254	TrkA-N domain	2	0.00441	MLOC_16944.1,MLOC_57408.2
PF00560	Leucine rich	5	0.0166	MLOC_16613.8,MLOC_56473.2,
	repeat			MLOC_73066.1,MLOC_80515.1,
				MLOC_81219.2
PF00069	Protein	9	0.0274	MLOC_16613.8,MLOC_22145.2,
	kinase			MLOC_22271.1,MLOC_3013.1,
	domain			MLOC_56473.2,MLOC_61362.2,
				MLOC_73066.1,MLOC_80515.1,
				MLOC_81219.2
PF07885	Ion channel	2	0.0274	MLOC_14178.1,MLOC_81045.1
PF13855	Leucine rich	6	0.0274	MLOC_10527.1,MLOC_16613.8,
	repeat			MLOC_20132.1,MLOC_56473.2,
				MLOC_80515.1,MLOC_81219.2
PF00538	Linker histone	2	0.046	MLOC_10278.1,MLOC_34636.1
	H1 and H5 family

Table 4 INTERPRO protein domains and features for potassium transporters and its associated protein.

Pathway	Pathway	Observed	False	Matching proteins
ID	description	gene	discovery	in your network (IDs)
		count	rate
IPR003855	Potassium	14	$6.61\times 10^{-28}$	MLOC_10758.2,MLOC_11567.1,
	transporter			MLOC_11780.6,MLOC_16004.3,
				MLOC_30826.1,MLOC_37174.3,
				MLOC_53246.1,MLOC_58287.2,
				MLOC_62643.1,MLOC_63991.1,
				MLOC_66784.2,MLOC_70288.1,
				MLOC_71693.1,MLOC_74565.1
IPR006153	Cation/H+	4	0.000139	MLOC_10701.1,MLOC_16944.1,
	exchanger			MLOC_4602.3,MLOC_57408.2
IPR013210	Leucine-rich	8	0.000139	MLOC_10527.1,MLOC_16613.8,
	repeat-			MLOC_20132.1,MLOC_56473.2,
	containing N-			MLOC_64392.1,MLOC_73066.1,
	terminal,			MLOC_80515.1,MLOC_81219.2
	plant-type
IPR018422	Cation/H+	3	0.000872	MLOC_10701.1,MLOC_36509.1,
	exchanger,			MLOC_4602.3
	CPA1 family
IPR017441	Protein	11	0.00132	MLOC_10527.1,MLOC_16613.8,
	kinase, ATP			MLOC_20132.1,MLOC_22145.2,
	binding site			MLOC_22271.1,MLOC_3013.1,
				MLOC_56473.2,MLOC_61362.2,
				MLOC_73066.1,MLOC_80515.1,
				MLOC_81219.2
IPR001754	Orotidine	2	0.00332	MLOC_14891.1,MLOC_6793.2
	5-phosphate
	decarboxylase
	domain
IPR003148	Regulator of	2	0.00332	MLOC_16944.1,MLOC_57408.2
	$K^{+}$ conductance,
	N-terminal
IPR005715	Glutamate	2	0.00332	MLOC_37763.1,MLOC_57545.1
	5-kinase/delta-1-
	pyrroline-5-
	carboxylate
	synthase
IPR014732	Orotidine 5-	2	0.00332	MLOC_14891.1,MLOC_6793.2
	phosphate
	decarboxylase
IPR018089	Orotidine 5-	2	0.00332	MLOC_14891.1,MLOC_6793.2
	phosphate
	decarboxylase,
	active site
IPR019797	Glutamate 5-	2	0.00332	MLOC_37763.1,MLOC_57545.1
	kinase,
	conserved site
IPR023031	Orotate e	2	0.00332	MLOC_14891.1,MLOC_6793.2
	phosphoribosy
	ltransferas
IPR001611	Leucine-rich	7	0.00796	MLOC_10527.1,MLOC_16613.8,
	repeat			MLOC_20132.1,MLOC_56473.2,
				MLOC_73066.1,MLOC_80515.1,
				MLOC_81219.2
IPR003280	Two pore	2	0.00796	MLOC_14178.1,MLOC_81045.1
	domain
	potassium
	channel
IPR031144	Two pore	2	0.00796	MLOC_14178.1,MLOC_81045.1
	domain
	potassium
	channel, plant
IPR008271	Serine/threonine-	10	0.0125	MLOC_16613.8,MLOC_20132.1,
	protein kinase,			MLOC_22145.2,MLOC_22271.1,
	active site			MLOC_3013.1,MLOC_56473.2,
				MLOC_61362.2,MLOC_73066.1,
				MLOC_80515.1,MLOC_81219.2
IPR001057	Glutamate/	2	0.0132	MLOC_37763.1,MLOC_57545.1
	acetylglutamate
	kinase
IPR029872	Na${}^{+}$/H${}^{+}$	2	0.0132	MLOC_10701.1,MLOC_4602.3
	exchanger
	NHX-type,
	plants
IPR000719	Protein	11	0.0183	MLOC_10527.1,MLOC_16613.8,
	kinase			MLOC_20132.1,MLOC_22145.2,
	domain			MLOC_22271.1,MLOC_3013.1,
				MLOC_56473.2,MLOC_61362.2,
				MLOC_73066.1,MLOC_80515.1,
				MLOC_81219.2
IPR013099	Potassium	2	0.0198	MLOC_14178.1,MLOC_81045.1
	channel
	domain
IPR011009	Protein	11	0.0237	MLOC_10527.1,MLOC_16613.8,
	kinase-like			MLOC_20132.1,MLOC_22145.2,
	domain			MLOC_22271.1,MLOC_3013.1,
				MLOC_56473.2,MLOC_61362.2,
				MLOC_73066.1,MLOC_80515.1,
				MLOC_81219.2
IPR004709	Na${}^{+}$/H${}^{+}$	2	0.027	MLOC_10701.1,MLOC_4602.3
	exchanger
IPR030184	WAT1-related	3	0.0434	MLOC_37297.1,MLOC_38597.1,
	protein			MLOC_62717.1
IPR000620	EamA domain	3	0.0442	MLOC_37297.1,MLOC_38597.1,
				MLOC_62717.1
IPR005818	Linker histone	2	0.0442	MLOC_10278.1,MLOC_34636.1
	H1/H5,
	domain H15

Table 5 The average, minimum and maximum score for the analysis of the protein–protein interaction network by STRING.

	Automatedtextmining	Co-expression	Combined score	Database annotated	Edge betweenness	Experimentally determined interaction	Gene fusion	Homology	Neighborhood on chromosome	Phylogenetic co-occurrence
Average	0.39	0.0045	0.485	0.083	249.64	0.099	0.0004	0.035	0.031	0.0024
Maximum	0.50	0.9960	0.999	0.896	3938.0	0.828	0.0300	0.981	0.517	0.5270
Minimum	0.00	0.0000	0.400	0.000	2.0000	0.000	0.0000	0.000	0.000	0.0000

The centrality measures considered for protein–protein interaction network for potassium transporter in barley were Betweenness, Bridging, Closeness, Degree, Eccentricity, Eigenvector, Radiality, and Stress. The average, minimum and maximum values for all the centrality measurements for the node is presented in Table 6 for the network formed between the potassium transporters. The centrality measures for the top five proteins are listed in Table 7.

Table 6 Cytoscape analysis of protein–protein interaction network for the protein (node) and its centrality measurement.

	Betweenness	Bridging	Closeness	Degree	Eccentricity	Eigenvector	Radiality	Stress
Average	79.3650	2.4948	0.0073	14.2222	0.31482	0.09494	2.7199	1214.3
Maximum	1369.36	17.627	0.0104	29.0000	0.50000	0.23868	3.4516	15124
Minimum	0.00000	0.0000	0.0049	1.00000	0.25000	0.00175	1.7258	0.0000

Table 7 Top five proteins based on the centrality measures.

Name	Betweenness	Bridging	Closeness	Degree	Eccentricity	Eigenvector	Radiality	Stress
MLOC_6793.2	444.62	6.2	0.01	28	0.5	0.15	3.45	5932
MLOC_14891.1	444.62	6.2	0.01	28	0.5	0.15	3.45	5932
MLOC_16944.1	519.25	7.85	0.01	27	0.5	0.16	3.44	7288
MLOC_57408.2	519.25	7.85	0.01	27	0.5	0.16	3.44	7288
MLOC_63991.1	168.12	3.88	0.01	29	0.3	0.24	3.19	3438

List 1. The proteins having above average values for the centrality measures

Betweenness

MLOC_6793.2; MLOC_14891.1; MLOC_57545.1; MLOC_37763.1; MLOC_16944.1; MLOC_57408.2;MLOC_74565.1; MLOC_63991.1; MLOC_16004.3; MLOC_66784.2

Bridging

MLOC_6793.2; MLOC_14891.1; MLOC_57545.1; MLOC_37763.1; MLOC_16944.1; MLOC_57408.2;MLOC_74879.2; MLOC_61362.2; MLOC_68594.1; MLOC_4602.3; MLOC_74565.1; MLOC_63991.1;MLOC_16004.3; MLOC_53246.1; MLOC_11780.6; MLOC_66784.2; and MLOC_62861.3

Closeness

MLOC_6793.2; MLOC_14891.1; MLOC_57545.1; MLOC_37763.1; MLOC_80515.1; MLOC_20132.1;MLOC_81219.2; MLOC_56473.2; MLOC_16613.8; MLOC_10527.1; MLOC_62057.1; MLOC_16944.1;MLOC_57408.2; MLOC_74879.2; MLOC_61362.2; MLOC_68594.1; MLOC_4602.3; MLOC_36509.1;MLOC_10701.1; MLOC_74565.1; MLOC_63991.1; MLOC_3013.1; MLOC_22271.1; MLOC_22145.2;MLOC_10758.2; MLOC_51219.1; MLOC_53246.1; and MLOC_66784.2

Degree

MLOC_6793.2; MLOC_14891.1; MLOC_57545.1; MLOC_37763.1; MLOC_80515.1; MLOC_20132.1;MLOC_81219.2; MLOC_56473.2; MLOC_16613.8; MLOC_10527.1; MLOC_81045.1; MLOC_62057.1;MLOC_14178.1; MLOC_16944.1; MLOC_57408.2; MLOC_4602.3; MLOC_36509.1; MLOC_10701.1;MLOC_69266.3; RGA1; MLOC_20651.1; MLOC_5059.1; MLOC_63991.1; MLOC_51219.1; MLOC_22108.1; MLOC_53246.1; and MLOC_66784.2

Eccentricity

MLOC_6793.2; MLOC_14891.1; MLOC_57545.1; MLOC_37763.1; MLOC_80515.1; MLOC_20132.1;MLOC_81219.2; MLOC_56473.2 MLOC_16613.8; MLOC_10527.1; MLOC_62057.1; MLOC_16944.1;MLOC_57408.2; MLOC_74879.2; MLOC_61362.2; MLOC_68594.1; MLOC_4602.3; MLOC_36509.1;MLOC_10701.1; MLOC_74565.1; MLOC_63991.1; MLOC_3013.1; MLOC_22271.1; MLOC_22145.2;MLOC_10758.2; MLOC_16004.3; MLOC_51219.1; MLOC_70288.1; MLOC_11567.1; MLOC_71693.1;MLOC_62643.1; MLOC_30826.1; MLOC_37174.3; MLOC_58287.2; MLOC_53246.1; MLOC_11780.6; andMLOC_66784.2

Eigenvector

MLOC_6793.2; MLOC_14891.1; MLOC_80515.1; MLOC_20132.1; MLOC_81219.2; MLOC_56473.2;MLOC_16613.8; MLOC_10527.1; MLOC_81045.1; MLOC_62057.1; MLOC_14178.1; MLOC_16944.1;MLOC_57408.2; MLOC_4602.3; MLOC_36509.1; MLOC_10701.1; MLOC_69266.3; RGA1; MLOC_20651.1; MLOC_5059.1; MLOC_63991.1; MLOC_3013.1; MLOC_22271.1; MLOC_22145.2; MLOC_51219.1;MLOC_22108.1; MLOC_53246.1; and MLOC_64392.1

Radiality

MLOC_6793.2; MLOC_14891.1; MLOC_57545.1; MLOC_37763.1; MLOC_80515.1; MLOC_20132.1;MLOC_81219.2; MLOC_56473.2; MLOC_16613.8; MLOC_10527.1; MLOC_62057.1; MLOC_16944.1;MLOC_57408.2; MLOC_74879.2; MLOC_61362.2; MLOC_68594.1; MLOC_4602.3; MLOC_36509.1;MLOC_10701.1; MLOC_74565.1; MLOC_63991.1; MLOC_3013.1; MLOC_22271.1; MLOC_22145.2;MLOC_10758.2; MLOC_51219.1; MLOC_71693.1; MLOC_53246.1; and MLOC_66784.2

Stress

MLOC_6793.2; MLOC_14891.1; MLOC_16944.1; MLOC_57408.2; MLOC_63991.1; MLOC_53246.1;and MLOC_66784.2

Discussion

The interactions in the living systems are usually depicted as networks. Network biology is a part of systems biology which enables us to understand the property of individual protein by analyzing the interactions between them via graph theory. Protein–protein interaction network is a mathematical expression of relationship between them. It has very high biological significance as it is specific [8]. Based on centrality measures on the interaction between the potassium transporters in Hordeum vulgare (NCBI taxonomy Id is 4513) it was found that proteins MLOC_6793.2, MLOC_14891.1, MLOC_16944.1, MLOC_57408.2, and MLOC_63991.1 were the most essential for the network. Moreover these five proteins interact among them and forms a subgraph (Fig. 2). MLOC_63991.1 is found to be the most essential as it links other 4 proteins in the network.

Fig. 2 Network formed by five crucial proteins in the potassium transporter network.

The description of these proteins which are available in the literature are listed below:

MLOC_6793.2 has 482 amino acid sequences. Its molecular functions are orotate phosphoribosyl transferase activity and orotidine-5’-phosphate decarboxylase activity based on EMBL-EBI. It involves inasuridine-5’-phosphate biosynthesis pathway [9]. MLOC_6793.2 is a predicted protein also known asMLOC_6793.4, F2D3N8, MLOC_6793.5, and MLOC_6793.2. MLOC_14891.1 is a predicted protein with 477 amino acid sequences [a.k.a. M0UW37, MLOC_14891,F2E520_HORVD, and MLOC_14891.1]. It exhibits orotate phosphor ribosyl transferase activity, orotidine-5’-phosphate decarboxylase activity, ’de novo’ pyrimidine nucleobase biosynthetic process, and ’de novo’ UMP biosynthetic process based on InterPro and gene ontology [10]. MLOC_16944.1 is also known as M0V1P8_HORVD, AK356552, MLOC_16944, MLOC_16944.1. It was predicted as a functional partner of sodium/hydrogen exchanger. This predicted protein is made up of 535 amino acid sequences. MLOC_57408.2 is a Predicted protein with 1107 amino acid sequences [a.k.a. MLOC_57408.3, M0X6E4, and MLOC_57408.2]. It is a predicted functional partner of MLOC_30826.1, a potassium transporter [11]. MLOC_63991.1 is a monomer with 769 amino acid sequences. This protein is predicted and described as potassium transporter [a.k.a. MLOC_63991.2, BAJ85390.1, F2DCE1, and MLOC_63991.1] [10].

2 Conclusion

Analysis of proteins in crops has become fairly easy after the introduction of software such as Cytoscape, STRING, etc. Various cereal crops have been studied to improve the crop yield and nutritional value. Abiotic stresses such as salinity, drought, temperature, etc. alter the protein expression in plants. This study helps in identifying w proteins which are required to improve the plant tolerance to abiotic stresses.