Massive peptide sharing between viral and human proteomes.

Thirty viral proteomes were examined for amino acid sequence similarity to the human proteome, and, in parallel, a control of 30 sets of human proteins was analyzed for internal human overlapping. We find that all of the analyzed 30 viral proteomes, independently of their structural or pathogenic characteristics, present a high number of pentapeptide overlaps to the human proteome. Among the examined viruses, human T-lymphotropic virus 1, Rubella virus, and hepatitis C virus present the highest number of viral overlaps to the human proteome. The widespread and ample distribution of viral amino acid sequences through the human proteome indicates that viral and human proteins are formed of common peptide backbone units and suggests a fluid compositional chimerism in phylogenetic entities canonically classified distantly as viruses and Homo sapiens. Importantly, the massive viral to human peptide overlapping calls into question the possibility of a direct causal association between virus-host sharing of amino acid sequences and incitement to autoimmune reactions through molecular recognition of common motifs.

Introduction

Protein sequences of the human proteome as well as of a number of viral proteomes have become available in databanks, offering an opportunity for a thorough comparative analysis of their reciprocal inter-relationship(s). This area of inquiry is of special interest because of our persistent ignorance of the mechanisms at the basis of viral pathogenesis and virus–host interactions [1], [19]. To define the molecular structural determinants possibly involved in viral virulence and human susceptibility, we have undertaken a systematic analysis of a large set of viral proteomes searching for amino acid sequences shared with the human proteome.

Specifically, the study was designed to answer two main questions: (i) what is the quantitative dimension of short-peptide sequence sharing between viruses and human proteomes? (ii) How many human proteins harbor viral peptide modules? The two questions are of paramount importance in the context of autoimmune diseases. Indeed, it is a common belief that an autoimmune reaction is mostly caused by a host receiving an antigen that has amino acid homology/similarity with amino acid sequences in self-antigens of the host [20]. This may result in the host immune system attacking the organs and tissues expressing the self-antigens with the shared sequences [23]. In this context and given the sustained increase in the incidence of autoimmune diseases [2], [8], [18], [22], the mathematical quantification of peptide overlap extent between viruses and humans is essential to understand the role of structural viral similarity in the pathogenesis of autoimmunity. Here we report a virus versus human comparative screening that reveals a massive, indiscriminate, unexpected pentapeptide overlapping between viral and human proteomes.

Methods

The viral proteomes analyzed in this study were downloaded from www.ebi.ac.uk/genomes/virus.html and the human proteome obtained from UniProtKB (http://www.ebi.ac.uk/integr8). The human proteome originally contained 37,993 proteins at the time of download, but we filtered out (1) two viral contaminants (i.e. viral proteomes which were being expressed in human cells), and (2) duplicated sequences and fragments. After the filtering, we were left with a human proteome consisting of 36,103 unique proteins.

Similarity analyses were conducted on 30 viral proteomes for a total of 717 viral proteins, equal to 302,667 amino acids. A similarly sized control set of 30 human protein samples (for a total of 686 human proteins, equal to 302,520 amino acids) was analyzed for inter-human overlapping. More precisely, for each viral proteome size, we constructed an artificial human sub-proteome drawn from the human protein set. The proteins were selected at random without replacement, and assigned to an artificial sub-proteome at random. We kept adding human proteins to each artificial human sub-proteome until adding the next protein would have caused the total number of amino acids in the artificial sub-proteome to exceed to the number of amino acids in the corresponding viral proteome. This way we eliminated possible bias in the comparison. The 30 artificial sub-proteomes are random samplings of human proteins and represent cross-sections of all human proteins. The list of the human proteins forming the 30 artificial human sub-proteomes is detailed as Swiss-Prot entries in Table 3.

Table 3

List of the human proteins forming the 30 artificial human sub-proteomes used as controls

Sub-proteome	Proteins (Swiss-Prot accession number)
1	Q8NAW6; Q6IBU4; ARFP1_HUMAN; SODM_HUMAN; AIF1_HUMAN; MAGB1_HUMAN; Q6ZNA3
2	Q6ZV29; Q86TW9; Q6UY40; Q9UI73
3	Q70T18; Q5TCI3; GALT_HUMAN; LV1H_HUMAN; Q6ZUC0; Q9H0S3; TRXR2_HUMAN; Q5TG59; Q16367
4	Q6NT40; Q5HY76; Q6EKI8; Q9BXM4; Q5T270; Q9UH93; Q6ZTB8; Q6ZSJ1; Q15217
5	Q6IPM5; GPR39_HUMAN; Q6ZRY8; Q96QH2; Q4VXA4; S10AE_HUMAN
6	Q6ZQW9;Q96P35;Q6ZSD3;Q8WVE6; Q6ZN23; Q5H9B2; Q8IWT5
7	Q59G50; Q3YL75; Q9BXX2; Q59GQ5
8	DSCR8_HUMAN; PK3CA_HUMAN; Q6PDB3; Q8TBZ2; Q8TBU1; PHP14_HUMAN; Q9NR69
9	LAP2A_HUMAN; O75100; Q8IYA8; Q9NWL9; Q6ZQN3; ZNF73_HUMAN; VEGFC_HUMAN; Q96FS0; Q5TBJ1; Q8IZX3; Q6AI09; Q96S91
10	Q5JV79; ATPK_HUMAN; Q6ZTT6; Q6IQ33; SYT9_HUMAN; Q5JXP8; Q71SF7; Q5SYF3; Q6PJ80
11	Q53ET8; Q12771; DDX31_HUMAN; Q66K28; GNA12_HUMAN; Q8TC57; DAX1_HUMAN; Q9BPY1
12	Q6GTM5; PRS23_HUMAN; Q5VXF4; DIAP3_HUMAN; LRC32_HUMAN; Q5W0C9; Q8N4M7; TIM9_HUMAN; Q6ZSL6; Q5VZP2; CK051_HUMAN
13	Q6ZVP3; ADH4_HUMAN; Q9BUR5; CP1B1_HUMAN; Q8TEG4; Q86YC2; KRA63_HUMAN; Q5JYC0; Q6PJ81; Q9P1J6; Q8N5A8
14	Q6IEE5; Q96CG3; Q59EI7; Q5TFG5; Q6NUM0; Q7L0X2; Q8N812; NLTP_HUMAN; Q29831; Q9NRI6
15	GPR21_HUMAN; PDCL3_HUMAN; Q5JX47; Q9Y4Q5; Q86WR9; 1B47_HUMAN; TM9S4_HUMAN; HUNIN_HUMAN; Q6ZTA6; Q6P519; Q4G178; Q8WUE9
16	ATF1_HUMAN; Q6ZSL8; Q5T0J3; Q8NB08; Q6MZW2; Q86T62; Q4G161; Q96HQ4; TSH3_HUMAN; Q6ZNA9; Q5TG33
17	OR5U1_HUMAN; Q9BRW6; FBX2_HUMAN; Q5TAE7; Q5T011; ELOV2_HUMAN; Q8NC43; Q5JR89
18	Q6ZRR8; Q5T435; Q6NUS8; CX033_HUMAN; ALG1_HUMAN; Q6ZUT0; Q86UH7; NCKX5_HUMAN; Q9BRY8; RT34_HUMAN; PODO_HUMAN; Q6ZNI2; Q6UXQ0
19	Q6ZTL0; Q6H9L7; Q96I32; PIPNA_HUMAN; Q9H5L8; Q5W0W3; Q9UMD0; Q5T9C4; Q5VU34; Q8N996; TRI11_HUMAN; Q5T884; Q6VEP3
20	VPS16_HUMAN; GLUC_HUMAN; Q5TI49; DAF_HUMAN; Q4G1H0; Q96KS6; Q5JX17; Q9H8F6
21	Q5VZB4; DNAL4_HUMAN; Q6PD71; ATN1_HUMAN; LRMP_HUMAN; Q53FG3; OR2I1_HUMAN; Q6NVW6; DPEP3_HUMAN; Q96NA9; DCPS_HUMAN
22	GAK14_HUMAN; Q969I2; IL17D_HUMAN; Q6ZWP8; Q5JUX3; Q8NAN0; Q8NEC4; Q9NVL7; Q9H7G9; Q5SXN7; Q6ZVQ9
23	Q5TAV5; Q4VHE5; CSMD3_HUMAN; Q6ZR54; Q86XE7; BCLW_HUMAN; SPR2B_HUMAN; Q5SUL1
24	Q5T8C4; Q9H066; SO1B1_HUMAN; MLF2_HUMAN; Q6ZSJ6; Q3KRG0; Q9BWU4; Q5W0G7; Q8N9M6; Q5HYJ7; GP144_HUMAN; Q6ZUR8; Q9NX18; CAR10_HUMAN; Q6AI43; Q5VZL8
25	Q562N1; Q71RA6; CF060_HUMAN; Q9NYJ1; NFE2_HUMAN; APEX1_HUMAN; Q9BRE8; Q8TEB3; KLF11_HUMAN; Q8NH86; Q71VA3; Q8N7V1; Q5T694; Q96EK9; PRGB_HUMAN
26	GP160_HUMAN; Q6ZTQ0; KPSH1_HUMAN; DAZ2_HUMAN; Q9UPD4; Q5TBP5; ZNF6_HUMAN; Q8N6V7; KGP1B_HUMAN; AMYP_HUMAN; KRA45_HUMAN; Q6PI78; Q68D85; TRI10_HUMAN; MAGC1_HUMAN; PRM2_HUMAN; STAC_HUMAN; P5I11_HUMAN; O19519; Q9Y6P1; LIPS_HUMAN; Q9NSQ0; Q5T5R8; Q2VU70; Q13901; Q9BWL2; Q5T4D3; Q5QPV6; Q9UHS2; Q9H6W3; Q8N930; Q6NSH2; Q2M2H8; Q5SR59; LY6E_HUMAN
27	Q8IY34; Q6XYE6; Q8IVW8; GCC1_HUMAN; LBH2_HUMAN; NEUU_HUMAN; Q71RC9; Q6P3S1; SC5A4_HUMAN; Q8WVS4; M3K2_HUMAN; Q4G186; Q572P5; CPNE8_HUMAN; ITM2B_HUMAN; Q6ZR04; Q6ZT02; TCP10_HUMAN; Q2HIZ2; Q86UC7; LRC24_HUMAN; Q3SX69; Q5JXA9; Q96CG5; Q5JZG9; MYO3B_HUMAN; Q15156; Q86TU2; Q9Y5L9; CG010_HUMAN; Q86VM9; Q5T932; Q8N7D3; SPG11_HUMAN; UB2V1_HUMAN; Q9Y2A3; BR44_HUMAN; Q8NBM8; Q6ZUE1; Q562S5; Q9GZU2; Q8N7G4; Q96DU8; Q8N7E6; Q14560; Q5SYX8; Q96KH8; Q96GK3; ATP5H_HUMAN; Q5TDQ5; SURF6_HUMAN; Q86TC9; Q3MI86; Q96NT9; Q7Z4A2; CCD70_HUMAN; STX1C_HUMAN; Q5VWX1; Q86TG5; CD016_HUMAN; Q59H22; Q6ZV55; PREX1_HUMAN; GOGA6_HUMAN; Q6IFP4; PRPS2_HUMAN; ZN337_HUMAN; Q6ZNV9; PUR9_HUMAN; UFC1_HUMAN; WBS16_HUMAN; Q8NCB2
28	Q8N944; Q6ZSG9; KV1V_HUMAN; Q9Y2V8; CN102_HUMAN; ROM1_HUMAN; Q6P3X8; FMO5_HUMAN; CTLA4_HUMAN; Q8NB05; Q7Z444; Q6ZTQ4; NP1L4_HUMAN; Q8TCI8; GAK1_HUMAN; CD44_HUMAN; OLIG2_HUMAN; MK11_HUMAN; Q6X4T0; Q5H8A3; Q9BV16; HNF3G_HUMAN; CENPF_HUMAN; DLK_HUMAN; TF_HUMAN; BRSK1_HUMAN; NARG1_HUMAN; Q9HCY0; Q6ZTW1; PLAC8_HUMAN; ARSK_HUMAN; RBPSL_HUMAN; Q5VFH8; Q5SZI4; Q4G0S2; Q8N1P4; SPDYC_HUMAN; Q8N5X6; WBS22_HUMAN; Q9BR23; Q8N6P1; Q5TAH2; Q15301; CADH5_HUMAN; FKBP4_HUMAN; KCNE4_HUMAN; Q9Y6X4; Q5TE79; GPC5B_HUMAN; Q5T2T6; IL1F8_HUMAN; Q9BTD8; Q9NPQ3; SEC62_HUMAN; CCNH_HUMAN; Q6ZV71; OR2L5_HUMAN; Q9NWB6; Q5T7H5; Q8WUU2; Q9H9F7; RM49_HUMAN; RS11_HUMAN; Q9Y2A1; Q8N0U6; Q64FX8; S6A18_HUMAN; SYN3_HUMAN; Q496M3; Q6ZUE9; Q9P274; CTR2_HUMAN; Q69YS5; TKN1_HUMAN; Q3MIV1; Q59FM3; Q3ZM63; Q9H4G2; O43410; Q86TS7; Q6IAE4; NAF1_HUMAN; Q49AN6; TNNC2_HUMAN; TNMD_HUMAN; Q3SY69; RALA_HUMAN; CAN12_HUMAN; Q6ZR90; Q29RV7; Q2M3R7; Q6IN85; ATPG_HUMAN; TBC13_HUMAN; Q6ZNL3; Q6IV50; Q5JY14; Q6ZSV2
29	Q6P2D1; Q9P1E4; RHPN2_HUMAN; Q8N657; Q5SUL4; DOCK3_HUMAN; PLOD1_HUMAN; SEL1L_HUMAN; O43379; Q6S382; STX4_HUMAN; O15281; CP2A6_HUMAN; Q9H2Q4; Q5TF13; Q6UX27; Q5JR60; Q59FA5; Q96LT2; K2C6B_HUMAN; GBRA2_HUMAN; CEBPG_HUMAN; DCBD2_HUMAN; CO6A2_HUMAN; IGF1R_HUMAN; Q8N139; Q86W07; JIP2_HUMAN; Q86SE5; Q6IF87; Q9H4T6; FBLN3_HUMAN; 1C06_HUMAN; MOB2_HUMAN; Q96RL5; Q9H5K0; NEUR4_HUMAN; Q8N1Y0; SPAG6_HUMAN; CRBB1_HUMAN; THNSL_HUMAN; Q9NW61; 9KD_HUMAN; Q92638; RCBT1_HUMAN; IMDH2_HUMAN; Q5VYN8; Q6UXJ7; Q2QGD7; Q96QS7; FHL3_HUMAN; Q59FX8; O00318; Q13584; ATRN_HUMAN; Q5VXJ0; O00172; Q8TB16; FBX47_HUMAN; Q59EY9; Q96D65; ABCC9_HUMAN; Q86T11; Q96SS4; Q6PCB0; Q7Z5Y7; Q6IA40; RB33A_HUMAN; TAAR2_HUMAN; ZNHI1_HUMAN; Q6MZG7; Q5VZ52; CI074_HUMAN; TMM28_HUMAN; Q53P42; RPA2_HUMAN; OR2G3_HUMAN; Q9H600; Q6PJ97; ZDH15_HUMAN; Q6RGF6; MTG8R_HUMAN; Q9H021; Q5D038; LAGE3_HUMAN; APMAP_HUMAN; Q6ISH0; Q9NXC2; PO3F2_HUMAN; Q96JN3; TNF15_HUMAN; Q71RE2; Q5V9X9; GSTM2_HUMAN; Q6ZUT7; Q13104; CTGE4_HUMAN; CASP2_HUMAN; Q5JVN7; Q96N29; Q8NF74; Q8NAC2; NDUB7_HUMAN; Q9UCY0; Q59GJ8; Q5JPH6; Q8TC08; Q5T2D2; Q5SXI0; Q9H3L6; Q5T2I9
30	PIAS3_HUMAN; MP2K5_HUMAN; Q96AZ4; Q96LL3; SIA7A_HUMAN; Q86W69; Q6ZW49; ARSG_HUMAN; Q6ZVF5; Q5T0B9; OLR1_HUMAN; Q5T352; Q96PN1; AIM1_HUMAN; Q8NGL5; CCD37_HUMAN; O95082; Q5ST44; PTPRU_HUMAN; SIGL5_HUMAN; Q96K25; Q4VX17; Q8IUI0; CACB4_HUMAN; Q6PIG1; TIMD1_HUMAN; O75799; Q6ZT72; Q8N9J9; Q8TAJ0; Q6PJ41; UBR1_HUMAN; Q69YL4; Q3V6T2; PART1_HUMAN; BTNL3_HUMAN; Q5JTE1; Q8WYS4; Q96GA7; Q8WXV1; Q68D36; RNAS9_HUMAN; Q5VXV0; OR5P3_HUMAN; DKK4_HUMAN; Q9NT50; Q6S376; OR6K2_HUMAN; Q68DF6; EPC1_HUMAN; Q9BZH2; Q96MJ8; Q6ZVE4; Q8WYG5; ETV2_HUMAN; Q6UXY4; GPC5D_HUMAN; Q6U8A4; Q6ZMM8; Q5QPJ9; Q9BZU3; Q9UDD7; Q8TAV7; Q86VP5; O4F15_HUMAN; RB3GP_HUMAN; Q6ZSQ0; Q9P073; UBE2T_HUMAN; Q5JUP7; Q9P035; Q562W8; STAC3_HUMAN; Q5T7F9; Q6IPW8; SALL2_HUMAN; Q9H3F1; Q96M24; Q6ZRX3; Q2M3I1; Q9HBS1; Q5W5X9; Q8N5E1; OBP2A_HUMAN; Q6ZWA4; F102B_HUMAN; HES6_HUMAN; Q5JUU8; UTP20_HUMAN; TLR4_HUMAN; Q9UL85; Q496H8; HV2A_HUMAN; Q86VK9; Q8IV74; HXA9_HUMAN; Q68CS5; Q8TEH7; Q6ZNW8; TRPV1_HUMAN; Q8N3Q9; Q6P995; Q6EF02; FA47B_HUMAN; CI055_HUMAN; BAD_HUMAN; Q5T7E5; Q6LET9; Q5T6F2; Q5VWV2; PCD16_HUMAN; 3BHS7_HUMAN; CB013_HUMAN; Q59FA6; Q9H901; Q8N1Y8; Q9BVY2; Q6ZSI0; MYOTI_HUMAN; Q6P0L0; Q8IYC2; Q5SWJ3; SPDYA_HUMAN; Q9Y569; Q2QD09; Q6NXR2; Q5T3L7; Q8N2D3; Q5JR95; Q5TYV8

Sequence similarity analyses of each of the 30 viral proteomes (or the 30 artificial human sub-proteomes) to the human proteome were carried out using viral (or human) 5-mers sequentially overlapped by four residues. The scans were performed by custom programs written in C and utilizing suffix trees for efficiency [6]. The viral or human proteins were manipulated and analyzed as follows. The entire viral (or human) proteome was decomposed in silico to a set of 5-mers (including all duplicates). A library of unique 5-mers for each virus or human proteome was then created by removing duplicates. Next, for each 5-mer in the library, the entire human proteome was searched for instances of the same 5-mer. Any such occurrence was termed an overlap. Cursory analyses (e.g. identification of unique overlapping 5-mers, counts of unique overlapping 5-mers, counts of duplications) were performed using LINIX/UNIX shell scripts and standard LINUX/UNIX utilities. Data were plotted and linear least-squares regression was performed to determine whether any linear relationships exist between the level of overlap and the size of viral (or control) proteomes. The same procedure was applied when longer amino acid sequences (esa-, epta-, octapeptides, etc.) were used as probes in the similarity analyses.

The similarity analysis for the artificial human sub-proteomes was performed as for the viral proteomes, with one modification. The modification is that all the proteins in the artificial proteome were removed from the comparison human proteome when the overlap determination was done. E.g., the artificial human sub-proteome 9 contains 12 human proteins selected from the original set of 36,103 proteins comprehensively forming the human proteome. These 12 proteins were removed from the comparison set prior to determining the overlap of artificial human sub-proteome 9 versus human; i.e. for artificial proteome 9, the overlap comparison was between 12 proteins in the artificial proteome and the remaining 36,091 human proteins. And similar for each of the other 29 artificial proteomes.

Results

The viruses were chosen based on the following criteria: (1) known to be pathogenic to human; (2) of significant health impact (e.g. HCV, rhinovirus, HIV, mumps, measles, SARS, HPV, and polio); (2) phylogenetically different; (4) proteomes established to a significant degree of completeness. In addition, the viral proteomes were chosen to span a range of proteome sizes, with the smallest viral proteome being 1613 amino acids, the largest being 65,280 amino acids, and all the viral proteomes combined amounting to 302,667 amino acids. The viral proteomes are described in Table 1 .

Table 1

Description of the viral proteomes analyzed for similarity to human proteins

Tax Id	Virus description and abbreviation	Accession	Proteins	aa
10407	Hepatitis B virus (HBV)	X51970	4	1,613
10632	JC polyomavirus (JCV)	J02226	5	1,629
10798	Human parvovirus B19	AF162273	3	2,006
12131	Human rhinovirus 14 (HRV-14)	K02121	1	2,179
12080	Human poliovirus 1 (HPV-1)	AJ132961	1	2,209
434309	Saffold virus (SAF-V)	EF165067	1	2,296
333760	Human papillomavirus type 16 (HPV16)	K02718	8	2,452
11908	Human T-cell leukemia virus 1 (HTLV-I)	U19949	6	2,589
11103	Hepatitis C virus (HCV)	AJ132997	1	3,010
11041	Rubella virus	AF188704	2	3,179
11089	Yellow fever virus (YFV)	X03700	1	3,411
307044	West Nile virus (WNV)	AY842931	1	3,433
11676	Human immunodeficiency virus 1 (HIV-1)	X01762	9	3,571
11292	Rabies virus	M31046	5	3,600
11029	Ross River virus (RRV)	M20162	2	3,733
11709	Human immunodeficiency virus 2 (HIV-2)	X05291	9	3,759
162145	Human metapneumovirus (hMPV)	AF371337	9	4,163
93838	Influenza A virus (H5N1)	AF144300	10	4,467
11250	Human respiratory syncytial virus (HRSV)	AF013254	11	4,540
11216	Human parainfluenza virus 3 (HPIV3)	AB012132	6	4,842
11269	Lake Victoria marburgvirus	Z12132	7	4,846
11161	Mumps virus	AB040874	8	4,977
70149	Measles virus	AY486084	8	5,205
186538	Zaire virus	AF086833	9	5,493
63330	Hendra virus	AF017149	9	6,056
321149	SARS coronavirus (SARS-CoV)	AY864806	13	14,209
10376	Human herpesvirus 4 (HHV-4)	AY961628	69	34,911
10368	Human herpesvirus 6 (HHV-6)	X83413	112	44,720
10255	Variola virus	X69198	197	54,289
10359	Human herpesvirus 5 (HHV-5)	X17403	190	65,280
Total			717	30,2667

As controls, we used 30 similarly sized protein sets drawn from the human proteome. In brief, we constructed 30 artificial sub-proteomes populated by proteins randomly selected without replacement from the human proteome. The constraints we used were that (1) the size of each artificial sub-proteome was to approximate the size of one of the viral proteomes, and (2) that the combined size of all the artificial proteomes was to approximate the combined size (in amino acids) of the viral proteomes. The 30 resultant artificial human sub-proteomes are described in Table 2 . Table has four columns. The first is the artificial sub-proteome number. The second is the cumulative amino acid length of all the human proteins in that artificial sub-proteome. The third column is the cumulative amino acid length of the corresponding viral proteome. The fourth column is the number of human proteins in the artificial sub-proteome. For example, in our virus versus human analysis, information for hepatitis C virus (HCV) was used as follows. The virus has taxonomic ID 11103 and a proteome consisting of 3010 amino acids (see Table 1). Corresponding to this virus is the artificial human sub-proteome 9, which contains 12 human proteins which sum to a cumulative length of 2979 amino acids (i.e. just about the 3010 amino acids in the HCV proteome). And similar for each of the other 29 viral proteomes. The list of the human proteins forming the 30 artificial human sub-proteomes is detailed as Swiss-Prot entries in Table 3 .

Table 2

Size descriptions of the 30 artificial human sub-proteomes used as controls

Sub-proteome number	Sizea	Sizea of the corresponding viral proteome	Human proteins in the sub-proteomeb
1	1,595	1,613	7
2	1,616	1,629	4
3	1,997	2,006	9
4	2,142	2,179	9
5	2,186	2,209	6
6	2,290	2,296	7
7	2,433	2,452	4
8	2,585	2,589	7
9	2,979	3,010	12
10	3,156	3,179	9
11	3,383	3,411	8
12	3,427	3,433	11
13	3,554	3,571	11
14	3,594	3,600	10
15	3,721	3,733	12
16	3,759	3,759	11
17	4,127	4,163	8
18	4,437	4,467	13
19	4,528	4,540	13
20	4,827	4,842	8
21	4,812	4,846	11
22	4,945	4,977	11
23	5,200	5,205	8
24	5,492	5,493	16
25	6,037	6,056	15
26	14,203	14,209	35
27	34,894	34,911	72
28	44,655	44,720	98
29	54,263	54,289	111
30	65,683	65,280	130
Total	302,520	302,667	686

Amino acid number.

Composed by set of human proteins as detailed under Section 2 and in Table 3.

Sequence similarity analyses of each of the 30 viral proteomes (or the 30 human artificial sub-proteomes) to the human proteome were conducted using viral (or human) 5-mers sequentially overlapped by four residues. Basically, we used 5-mer sequences as probes to scan viral/human proteins against human proteome since pentapeptides are minimal structural units critically involved in biological/pathological interactions such as peptide–protein interaction and (auto)immune recognition ([5], [7], [17], [18], [21] and further Refs. therein). Longer amino acid sequences (esa-, epta-, octapeptides, etc.) were used as additional probes to control the similarity pattern.

Quantitative analysis of the pentapeptide overlapping of viral versus human proteomes

The numerical values that define virus-to-human similarity level are reported in Table 4 . The table documents that all of the 30 viral proteomes under analysis have high and wide pentapeptide overlapping to the human proteome. Only a limited number of viral pentamers are unique to the viruses, with no counterpart in the human proteome. The overlapping extent (in terms of percentage of unique viral 5-mers which occur in the human proteome, see column 6 in Table 4) is rarely less than 90%. That means that almost 90% of the viral pentamer peptides are widely, intensively and repeatedly scattered throughout the human proteome. Numerically, the viral versus human overlap is defined by 2,907,096 total matches. Also the viral overlapping distribution in the human proteome (as number of human proteins hosting viral 5-mers, column 5 in Table 4) is highest, being close to 100% with HHV-5 (i.e. HHV-5 pentamers are present in 35,708 human proteins out of the 36,103 ones which comprehensively form the human proteome).

Table 4

Viral versus human proteome overlap at the 5-mer level

Virusa	1	2	3b	4b	5	6
HBV	1,589	1,597	1,460	21,852	13,116	91.8
JCV	1,531	1,609	1,404	22,482	12,740	91.7
Human parvovirus B19	1,443	1,994	1,290	21,488	11,783	89.3
HRV-14	2,173	2,175	1,993	23,761	13,936	91.7
HPV-1	2,203	2,205	2,016	23,431	13,648	91.5
SAF-V	2,283	2,287	2,092	23,995	13,940	91.6
HPV16	2,419	2,420	2,245	28,948	15,361	92.8
HTLV-I	2,563	2,565	2,373	44,042	19,303	92.5
HCV	3,002	3,006	2,754	46,731	20,269	91.7
Rubella virus	3,154	3,171	2,898	51,859	20,401	91.8
YFV	3,400	3,407	3,069	43,245	19,505	90.2
WNV	3,424	3,429	3,087	42,670	19,702	90.1
HIV-1	3,082	3,535	2,792	35,568	17,487	90.5
Rabies virus	3,575	3,580	3,314	42,643	19,584	92.6
RRV	3,622	3,632	3,297	42,422	19,436	91.0
HIV-2	3,285	3,723	2,949	45,724	18,888	89.7
hMPV	4,120	4,127	3,779	52,915	20,688	91.7
H5N1	4,412	4,427	4,036	45,599	20,117	91.4
HRSV	4,483	4,496	4,116	46,540	19,878	91.8
HPIV3	4,807	4,818	4,418	52,934	21,131	91.9
Lake Victoria marburgvirus	4,808	4,818	4,439	67,051	22,915	92.3
Mumps virus	4,786	4,945	4,417	61,013	22,627	92.2
Measles virus	4,934	5,173	4,561	60,638	23,045	92.4
Zaire virus	4,865	5,457	4,487	56,577	22,236	92.2
Hendra virus	5,210	6,020	4,810	57,646	22,644	92.3
SARS-CoV	9,739	14,157	8,853	108,632	27,957	90.9
HHV-4	32,009	34,635	29,955	531,946	35,092	93.5
HHV-6	41,834	44,272	38,277	467,206	35,057	91.4
Variola virus	52,017	53,501	47,180	498,970	35,035	90.7
HHV-5	61,001	64,520	56,300	883,952	35,708	92.2
Allc	257,035	299,701	234,691	2,907,096

Human proteome formed by 36,103 proteins and 15,771,565 occurrences of 2,388,563 unique 5-mers. Column number refers to: (1) unique 5-mers in the viral proteome; (2) total number of 5-mers in the viral proteome (including multiple occurrences); (3) unique viral 5-mers occurring in the human proteome; (4) viral overlap occurrences in the human proteome (including multiple occurrences); (5) number of human proteins involved in overlap; (6) % of unique viral 5-mers which occur in the human proteome (i.e. 100 × column 3/column 1).

Abbreviations as in Table 1.

The results of linear regression analysis between columns 1 and 3, and 1 and 4 are: column 3 = 0.91811 × column 1 − 1.2272 (r = 0.99993). Column 4 = 12.636 × column 1 − 269.01 (r = 0.97452).

Obtained by combining all 30 viral proteomes into one viral proteome, and then computing the overlap with the entire human proteome.

Given the ample literature documentation and research data in support of the critical role exerted by pentapeptide modules in cell biology as well in antigen/antibody immunorecognition ([17], and Refs. therein), the data of Table 4 are impressive. They become even more significant, if possible, when compared to the control data obtained for inter-human pentapeptide overlapping illustrated in Table 5 . When 30 human artificial sub-proteomes were analyzed for pentapeptide overlapping to the entire human proteome, we obtained clear numerical evidence that the inter-human level of perfect 5-mer matching is of the same order of magnitude as the viral overlapping to human proteome detailed in Table 4, i.e. there are 3,713,010 inter-human versus 2,907,096 viral perfect 5-mer matches to the human proteome (see Table 5). Likewise, the number of human proteins involved in viral and inter-human overlaps is highest and practically identical in the numbers: human proteins are involved in the inter-human peptide overlapping for 697,373 times and in the viral versus human overlap for 633,229 times (see data from Table 4, Table 5, respectively). This indicates a massive, repeated, acritical usage of the same pentapeptide blocks in viral and human proteomes.

Table 5

Human versus human proteome overlap at the 5-mer level

Human sub-proteomea	1	2	3b	4b	5	6
1	1,566	1,567	1,496	27,014	14,381	95.5
2	1,598	1,600	1,576	26,352	13,850	98.6
3	1,937	1,956	1,885	44,087	17,222	97.3
4	2,100	2,106	2,064	30,025	14,400	98.2
5	2,058	2,162	1,986	40,374	17,653	96.5
6	2,131	2,262	2,098	62,564	16,698	98.4
7	2,289	2,417	2,227	37,796	17,303	97.2
8	2,556	2,557	2,475	33,387	16,439	96.8
9	2,858	2,931	2,721	81,650	20,105	95.2
10	3,067	3,120	3,042	48,918	19,069	99.1
11	3,306	3,351	3,198	59,666	21,568	96.7
12	3,355	3,383	3,271	68,549	22,697	97.4
13	3,478	3,510	3,377	56,469	21,221	97.0
14	3,529	3,554	3,417	60,138	21,490	96.8
15	3,663	3,673	3,596	88,229	22,454	98.1
16	3,626	3,715	3,545	96,827	22,061	97.7
17	4,084	4,095	3,958	72,451	23,645	96.9
18	4,374	4,385	4,112	68,579	24,055	94.0
19	4,423	4,476	4,295	79,283	24,472	97.1
20	4,751	4,795	4,735	70,296	22,686	99.6
21	4,646	4,768	4,555	115,916	26,284	98.0
22	4,886	4,901	4,850	95,959	25,348	99.2
23	5,048	5,168	4,740	72,400	23,453	93.8
24	5,408	5,428	5,144	101,686	26,713	95.1
25	5,941	5,977	5,841	128,904	27,751	98.3
26	13,143	14,063	12,700	290,022	32,681	96.6
27	33,281	34,606	32,460	664,539	35,181	97.5
28	42,683	44,263	41,535	787,778	35,375	97.3
29	51,865	53,804	50,075	895,672	35,489	96.5
30	62,219	65,156	60,506	1,103,962	35,629	97.2
Allc	254,808	299,749	246,322	3,713,010

All 30 artificial human sub-proteomes constitute 686 proteins and are numbered from 1 to 30. The comparison human proteome contained 36,103 proteins and 15,771,565 occurrences of 2,388,563 unique 5-mers. Column number refers to: (1) unique 5-mers in the artificial sub-proteome; (2) total number of 5-mers in the artificial sub-proteome (including multiple occurrences); (3) unique 5-mers from the artificial sub-proteome occurring in the human proteome; (4) occurrences in the human proteome of 5-mers from artificial sub-proteome (including multiple occurrences); (5) number of human proteins in the human proteome involved in overlap; (6) % of unique 5-mers from the artificial sub-proteome which occur in the human proteome (i.e. 100 × column 3/column 1).

Analogous to viral proteomes in size (see Table 1), and composed by set of human proteins as detailed in Table 3.

The results of linear regression analysis between columns 1 and 3, and 1 and 4 are: column 3 = 0.97083 × column 1 + 0.76628 (r = 0.99998). Column 4 = 17.921 × column 1 + 6278.6 (r = 0.99719).

Obtained by combining all 30 human sub-proteomes into one sub-proteome, and then computing the overlap with the entire original human proteome minus the proteins in the combined sub-proteomes.

Plotting the viral 5-mer occurrences in the human proteome as a function of the viral proteome length (see data under Table 4, columns 2 and 4) produces the graph illustrated in Fig. 1 . The following observations are worth noting: first, the level of viral overlaps to the human proteome is directly related to the length of the viral proteome by a linear relationship. The Pearson's correlation coefficient of the line is 0.97452, and represents an exceptionally strong linear relationship. Biologically, Fig. 1 suggests that peptide motif sharing is a constant property of the viral proteomes, exclusively depending on the viral proteome length and with no relationship to other structural and/or pathogenic viral features. As a note of interest, it can be seen that HTLV-1, Rubella virus, and HCV present a number of overlaps to the human proteome above the expected number of overlaps predicted by the linear regression line. This indicates that the massive, repeated, acritical, usage of the same pentapeptide blocks in viral and human proteomes is even more accentuated in the case of HTLV-1, Rubella virus, and HCV.

Fig. 1

Viral 5-mer occurrences in the human proteome as a function of viral proteome length (see data under Table 4, columns 1 and 4). The symbols refer to: (*), HTLV-1; (■), Rubella virus; (□), HCV; (+), other viral data point. The regression line (—) has an equation of y = 12.636x − 269.01, with a Pearson correlation coefficient (r value) of 0.97452. Both x- and y-axis are log scale.

Qualitative analysis of the pentapeptide overlapping of viral versus human proteomes

To understand the overlap usage in entities so distant in the evolutionary time and so enormously different in the evolutionary history such as viruses and humans, we investigated the profile of viral motif distribution along the human proteome. Representative histograms are reported in Fig. 2 . It can be seen that, independently of the virus size, the density of the viral 5-mer motif matches along the human proteome presents constant behavior, with virus portions endowed with high similarity alternating with portions scarcely represented in the human proteome. The same alternating behavior is shown by the histograms of inter-human overlapping by analyzing sub-proteomic sets of low, medium and large size (Fig. 3 ).

Fig. 2

Viral versus human pentapeptide overlapping: similarity profile of low-, medium- and high-molecular weight viral proteomes to the human proteome. (A) Human parvovirus B19 (total aa: 2006); (B) SARS coronavirus BJ202 (total aa: 14,209); (C) Variola virus (total aa: 54,289).

Fig. 3

Human versus human pentapeptide overlapping: similarity profile of low-, medium- and large-sized artificial human sub-proteomes to the human proteome. Human sub-proteome set (see also Table 2): (A) 4 (total aa: 2142); (B) 26 (total aa: 14,203); (C) 29 (total aa: 54,263).

Fig. 2, Fig. 3 confirm the commonality of pentapeptide block usage in viruses and humans and, more in general, document the existence of a basic structural platform in the protein world. As shown in Table 6, Table 7 , the viral versus human overlapping is still remarkable by using viral esa- or eptapeptide sequences as probes.

Table 6

Viral versus human proteome overlap at the 6-mer level

Virusa	1	2	3b	4b	5	6
HBV	1,589	1,593	496	1,394	1,224	31.2
JCV	1,527	1,604	461	1,468	1,301	30.1
Human parvovirus B19	1,442	1,991	431	1,341	1,150	29.8
HRV-14	2,174	2,174	579	1,456	1,270	26.6
HPV-1	2,204	2,204	619	1,483	1,288	28.0
SAF-V	2,285	2,285	622	1,392	1,280	27.2
HPV16	2,412	2,412	739	2,010	1,703	30.6
HTLV-I	2,559	2,559	967	3,261	2,658	37.7
HCV	3,005	3,005	1,025	4,195	3,099	34.1
Rubella virus	3,169	3,169	1,133	3,918	3,119	35.7
YFV	3,406	3,406	1,005	2,680	2,322	29.5
WNV	3,428	3,428	996	2,744	2,365	29.0
HIV-1	3,084	3,526	904	2,135	1,832	29.3
Rabies virus	3,575	3,575	1,162	2,635	2,195	32.5
RRV	3,613	3,613	1,069	2,708	2,301	29.5
HIV-2	3,283	3,714	1,006	4,653	2,780	30.6
hMPV	4,117	4,118	1,245	3,263	2,720	30.2
H5N1	4,408	4,417	1,191	2,674	2,332	27.0
HRSV	4,483	4,485	1,258	2,847	2,455	28.0
HPIV3	4,812	4,812	1,350	3,194	2,721	28.0
Lake Victoria marburgvirus	4,811	4,811	1,497	5,631	3,695	31.1
Mumps virus	4,787	4,937	1,463	3,734	3,017	30.5
Measles virus	4,937	5,165	1,585	3,823	3,208	32.1
Zaire virus	4,868	5,448	1,448	3,488	2,929	29.7
Hendra virus	5,210	6,011	1,516	3,538	3,035	29.0
SARS-CoV	9,767	14,144	2,704	9,438	5,770	27.6
HHV-4	32,634	34,566	12,358	50,013	18,445	37.8
HHV-6	42,432	44,160	12,126	35,793	16,847	28.5
Variola virus	53,136	53,304	13,870	33,911	16,167	26.1
HHV-5	63,115	64,330	21,873	87,931	23,629	34.6
Allc	282,932	298,966	87,224	259,655

Human proteome formed by 36,103 proteins and 15,734,725 occurrences of 8,247,275 unique 6-mers. Column number refers to: (1) unique 6-mers in the viral proteome; (2) total number of 6-mers in the viral proteome (including multiple occurrences); (3) unique viral 6-mers occurring in the human proteome; (4) viral overlap occurrences in the human proteome (including multiple occurrences); (5) number of human proteins involved in overlap; (6) % of unique viral 6-mers which occur in the human proteome (i.e. 100 × column 3/column 1).

Abbreviations as in Table 1.

The results of linear regression analysis between columns 1 and 3, and 1 and 4 are: column 3 = 0.31426 × column 1 − 42.237 (r = 0.98762). Column 4 = 1.0972 × column 1 − 877.68 (r = 0.93373).

Obtained by combining all 30 viral proteomes into one viral proteome, and then computing the overlap with the entire human proteome.

Table 7

Viral versus human proteome overlap at the 7-mer level

Virusa	1	2	3b	4b	5	6
HBV	1,586	1,589	66	102	93	4.1
JCV	1,523	1,599	61	108	101	4.0
Human parvovirus B19	1,440	1,988	49	120	104	3.4
HRV-14	2,173	2,173	60	118	100	2.7
HPV-1	2,203	2,203	69	139	123	3.1
SAF-V	2,283	2,283	35	51	51	1.5
HPV16	2,404	2,404	102	163	121	4.2
HTLV-I	2,553	2,553	143	283	215	5.6
HCV	3,004	3,004	129	403	369	4.2
Rubella virus	3,167	3,167	171	309	282	5.3
YFV	3,405	3,405	96	164	153	2.8
WNV	3,427	3,427	96	177	170	2.8
HIV-1	3,079	3,517	96	163	148	3.1
Rabies virus	3,570	3,570	132	208	192	3.6
RRV	3,595	3,595	119	184	169	3.3
HIV-2	3,278	3,705	121	1,347	535	3.6
hMPV	4,109	4,109	123	234	212	2.9
H5N1	4,400	4,407	96	147	139	2.1
HRSV	4,474	4,474	106	174	162	2.3
HPIV3	4,806	4,806	126	193	170	2.6
Lake Victoria marburgvirus	4,804	4,804	151	281	253	3.1
Mumps virus	4,780	4,929	142	214	204	2.9
Measles virus	4,931	5,157	158	257	244	3.2
Zaire virus	4,861	5,439	141	212	196	2.9
Hendra virus	5,204	6,002	126	219	203	2.4
SARS-CoV	9,759	14,131	299	790	699	3.0
HHV-4	32,670	34,497	1,899	7,050	3433	5.8
HHV-6	42,426	44,048	1,242	3,954	2236	2.9
Variola virus	53,044	53,107	1,638	2,760	1916	3.0
HHV-5	63,216	64,140	3,176	16,745	6030	5.0
Allc	284,918	298,232	10,863	31,737

Human proteome formed by 36,103 proteins and 15,697,964 occurrences of 10,431,975 unique 7-mers. Column number refers to: (1) unique 7-mers in the viral proteome; (2) total number of 7-mers in the viral proteome (including multiple occurrences); (3) unique viral 7-mers occurring in the human proteome; (4) viral overlap occurrences in the human proteome (including multiple occurrences); (5) number of human proteins involved in overlap; (6) % of unique viral 7-mers which occur in the human proteome (i.e. 100 × column 3/column 1).

Abbreviations as in Table 1.

The results of linear regression analysis between columns 1 and 3, and 1 and 4 are: column 3 = 0.042176 × column 1 − 36.723 (r = 0.95637); column 4 = 0.17329 × column 1 − 410.74 (r = 0.84397).

Obtained by combining all 30 viral proteomes into one viral proteome, and then computing the overlap with the entire human proteome.

Non-stochastic nature of the peptide overlapping between the viral and human proteomes under analysis

The results illustrated in Table 4, Table 6, Table 7 are indicative of a widespread peptide overlapping between viral proteins and the Homo sapiens proteome. In order to understand how the above reported data are mathematically governed, we explored the 30 viral proteomes and the 30 human sub-proteomes under analysis for the degree of internal redundancy and the viral versus human proteome overlapping at n-mer level (with n from 5 to 16 amino acids). The quantitative data we found are reported in Table 8 listing three orders of data: (a) the number of unique occurrences of n-peptides in the 30 viral (or the 30 human) proteome samples; (b) the total number of occurrences (i.e. including multiple occurrences) of n-peptides in the 30 viral (or the 30 human) proteome samples; and c) the relative viral versus human n-peptide overlaps.

Table 8

Actual versus theoretical n-peptide occurrences in viral and human proteins

Peptidea (n-mer)	Theoretical numberb (×106)	Actual occurrences
		1	2	3	4	5	6	7	8	9	10
5	3.2	257,035	299,701	42,666	254,808	299,749	44,941	42,647	56,230	234,691	2,907,096
6	64	282,932	298,966	16,034	286,515	299,058	12,543	4,394	5,089	87,224	259,655
7	1,280	284,918	298,232	13,314	289,592	298,367	8,775	455	617	10,863	31,737
8	25,600	284,857	297,498	12,641	289,744	297,676	7,932	80	163	1,668	8,805
9	512,000	284,497	296,764	12,267	289,486	296,985	7,499	30	81	656	4,035
10	10,240,000	284,022	296,030	12,008	289,096	296,294	7,198	14	27	437	1,311
11	204,800,000	283,494	295,296	11,802	288,629	295,603	6,974	8	9	352	661
12	4,096,000,000	282,926	294,562	11,636	288,121	294,912	6,791	5	5	292	449
13	81,920,000,000	282,341	293,829	11,488	287,578	294,223	6,645	3	3	240	316
14	1,638,400,000,000	281,735	293,096	11,361	287,017	293,535	6,518	1	1	204	254
15	32,768,000,000,000	281,116	292,363	11,247	286,441	292,847	6,406	0	0	171	207
16	655,360,000,000,000	280,478	291,630	11,152	285,842	292,160	6,318	0	0	148	176

Column number: (1) actual unique n-mers in the 30 viral proteomes; (2) actual n-mers in the 30 viral proteomes (including multiple occurrences); (3) number of repeated n-mers in the 30 viral proteomes; (4) actual unique n-mers in the 30 human sub-proteomes; (5) actual n-mers in the 30 human sub-proteomes (including multiple occurrences); (6) number of repeated n-mers in the 30 human sub-proteomes; (7) unique viral n-mers overlaps in the 30 human sub-proteomes; (8) total viral n-mers overlaps in the 30 human sub-proteomes (including multiple occurrences); (9) unique viral n-mers overlaps in the human proteome; (10) total viral n-mers overlaps in the human proteome (including multiple occurrences).

Peptide length, with n from 5 to 16 amino acids.

The number of possible amino acid combinations is given by 20.

The first set of data, column 1 through 6 in Table 8, provides evidence that the numerical values for uniquely expressed n-peptides are always lower than the actual total values for n-peptide occurrences. This is remarkable in light of the enormously high number of potential n-peptides which theoretically are available. As an example, in face of the possible 3,200,000 pentamers, both the 30 viral proteomes and the 30 human sub-proteomes present a high degree of repetitiveness in their 5-mer composition. In fact, the 30 viral proteomes and the 30 human sub-proteomes are respectively formed by a total of 299,701 and 299,749 5-mers and, although there is the possibility of forming 3,200,000 different pentapeptides, these total 5-mers present 42,666 and 44,941 repeated pentapeptides, in the viral and human proteins, respectively. The intra-viral (or intra-human) peptide redundancy strikingly persists at higher n-mer level, despite the exponentially increasing numbers of theoretically possible n-mers. The intra-repetitiveness is mathematically quantified in Table 8, columns 3 and 6, for the 30 viral proteomes and the 30 human sub-proteomes, respectively.

The second set of data illustrates the overlapping at n-peptide level (with n from 5 to 16 amino acids) from the 30 viral proteomes versus the 30 human sub-proteomes (Table 8, columns 7 and 8). Again, we note that, although there is a potential pentapeptide reservoir amounting to 3,200,000, still 42,647 of unique viral pentapeptides (16.6% of the total) appear in the human proteins forming the 30 human sub-proteomes. When considering the total viral pentapeptides including multiple occurrences (Table 8, column 8), the viral contribution to the 30 human sub-proteomes raises to 56,230 pentamers. These repeated viral overlaps in the human proteins appear to further support the existence of a defined peptide repertoire which is utilized (even repeatedly) in protein composition. A similar observation holds in considering the viral peptide overlapping to the 30 human sub-proteomes at higher n-mer level (Table 8, columns 7 and 8).

Finally, the third set of data documents the overlaps at n-peptide level (with n from 5 to 16 amino acids) from the 30 viral proteomes versus the entire human proteome (Table 8, columns 9 and 10). It can be seen that, independently of the vastness of the human protein sample, the extent of the viral overlap is relevant, especially when considering that the human proteome has the possibility of drawing from an enormous number of potential theoretical n-peptides. For example, notwithstanding the astonishingly great repertoire of theoretical possible undecapeptides (that is: 204,800,000 millions), nonetheless eight viral overlaps (one of which even repeated) are present in the sub-proteomic human samples. The numbers raise to 352 and 661 (including multiple occurrences of the same 11-mer) when considering the viral 11-mer overlaps in the entire human proteome.

Our conclusion is that the mathematical redundancy present in the protein world is not stochastic (i.e. is not pure random chance), but rather reflects strong peptide usage bias since certain peptides are repeatedly used (and shared) in (and among) viral and human proteins. It seems that peptide usage in the protein world (and, consequently, the degree of similarity, repetition, overlapping) is not dictated by pure mathematical laws of distribution. Rather, powerful constraints appear to be at work by forcing the use of restricted platforms of n-mers and privileging the repeated usage of the chosen ones. Possibly, as anticipated by preliminary data from our labs (Kusalik et al., manuscript in preparation), these powerful constraints able to influence and favor specific n-mer synthesis might be found among physico-chemical factors such as hydrophobicity, residue bulkiness, ΔG° of peptide bond formation, plus biological factors such as codon bias.

Discussion

The difference between biological entities such as viruses and cellular organisms is quite obvious when the functional roles of the proteins found in viruses and in cellular organisms are compared [24]. On the other hand, according to the data we present here, this obvious difference disappears at the peptide phenetic level. The mathematical quantification of the widespread and high pentapeptide similarity between viral and human proteomes is surprising, raising fundamental questions. In particular, the ample viral-human peptide sharing is significant to (auto)immune phenomena as well as to evolutionary pathways.

Indeed, in the context of the physio(patho)logical relationships between viruses and humans, it is mandatory to observe that short-peptide motifs can exert a central role in cell adhesion, signal transduction, hormone activity, regulation of transcript expression, and enzyme activity [3], [9], [10], [14], [15], [17]. Likewise, the antigen-antibody recognition process can reductionistically be circumscribed to interacting modules of five amino acid residues which, therefore, appear to be sufficient structural immunological determinants ([11], [12], [13], [17] and Refs. therein). Consequently, we notice that the data shown here call into question the possibility of a direct causal association between virus–host sharing of amino acid motifs and incitement of autoimmune reactions [20]. Indeed, the molecular mimicry hypothesis suggests that, when bacterial/viral agents share epitopes with a host's proteins, an immune response against the infectious agent may result in formation of cross-reacting antibodies that bind the shared epitopes on the normal cell and result in the auto-destruction of the cell. In the present case, the molecular mimicry hypothesis implies that viral infections should be a practically infinite source of autoimmunity diseases since this study demonstrates that viral 5-mer matches are disseminated throughout practically all the human proteome and each viral match is repeated almost more than 10 times (see Table 4). Consequently, autoimmune diseases should theoretically approach a 100% real incidence, since the 30 viruses we examined practically are more or less disseminated throughout the entire human species. Taking HCV as an example, we note that the virus versus human overlap analysis in Table 4 produces the mathematical evidence that HCV (as well as HTLV-1 and Rubella virus) are even “more similar” to the human proteins than that expected by the linear regression relationship reported in Fig. 3. The data reinforce and add to our previous report on the sharing of numerous perfect exact matches between HCV and human proteomes [16]. Since 2760 out of 3003 HCV 5-mers occur in the human proteome, the 46,731 occurrences include multiple/repeated matches in the human proteins. More exactly, each HCV 5-mer that overlaps with the human proteome are repeated an average of 16.5 times. Moreover, the 46,731 occurrences occur in 20,269 different human proteins (Table 4). That means that almost 60% of the 36,103 human proteins forming the H. sapiens proteome contain viral HCV pentamers. This datum is of cogent interest in the present worldwide advance of HCV infection, since it further excludes sequence similarity as a possible mechanism in HCV-associated autoimmunity phenomena and, at the same time, might address research towards new unexplored scenarios. These observations, of course, apply to almost all the viruses listed in Table 1.

When analyzed in the evolutionary context, the viral and human protein sets studied here yield a closely congruent overlapping pattern to the human proteome at the 5-mer level as well as at higher n-mer levels, indicating a common compositional mosaicism in viral and human proteins. The absence of specific phenetic n-mer clusterings in the viral versus human proteome (and vice versa) suggests a synergetic platform of development of non-equilibrium systems which can be traced back to the Prigoginian model rather than to the Darwinian evolutionary scheme, where harmful protein sequences are “selectively removed” whereas neutral or useful peptides are left alone. De facto, it seems that viruses and mammalians have drawn and draw from a common ancient melting pot of short-peptide modules in building up their proteomes, either simple or complex. Indeed, it seems that human evolution has paid very little attention to a peptide motif being viral or not. The reconstruction of the informational network relating all living organisms might be helped by an integration with the present data which recall a time when the totality of life on Earth was a simple heterogeneous community that shared only the ability to synthesize proteins with a ribosomal machine and that freely exchanged genetic material [4].