Download Interfering contexts of regulatory sequence elements

CABIOS
Vol. 12 no. 5 1996
Pages 423-429
Interfering contexts of regulatory sequence
elements
E.N.Trifonov
Abstract
Motivation: Although one would normally expect a given
regulatory element to perform best when it fully matches its
consensus sequence, this is generally far from being the case.
Usually, almost none of the actual sites fits the consensus
exactly, and some of those that do fit do not perform well. The
main reason for that is the very nature of the sequences and
the messages (codes) they contain. Normally, any given
stretch of the sequence with one or another regulatory site not
only carries this regulatory message, but several more
messages of various types as well. These messages overlap
with the regulatory element in such a way that the letter
(base) which actually appears in any given sequence position
simultaneously belongs to one or more additional codes.
Apart from numerous individual codes (sequence patterns)
specific for a given species or gene, there are many different
general (universal) sequence codes all interacting with one
another. These are the classical triplet code, DNA shape
code, chromatin code, gene splicing code, modulation code
and many more, including those that have not yet been
discovered. Examples of overlapping of different codes and
their interaction are discussed, as well as the role of
degeneracy of the codes and the sequence complexity as a
function of code density.
Contact: E-mail: [email protected]
Introduction
Recognition sites for various transcription factors in the DNA
sequences as well as, generally, specific sites of all types
appear as islands in the sea of an irrelevant 'random'
background, different for every individual site. Since many
positions in the consensus sequence of the site are frequently
degenerate, even the signal sequence itself is variable, the
variations being, again, seemingly random. The natural DNA
sequences (as well as RNA and protein sequences), however,
are far from being random. What was considered in earlier
days as the sea of 'junk' sequences is today shrinking to
remaining lakes and puddles of seemingly featureless and
functionless sequences which, most likely, also carry some
messages that are just not yet recognized.
Department of Structural Biology, The Weizmann Institute of Science,
Rehovot 76100, Israel
© Oxford University Press
As the reader will see below, many of those natural
sequences which are found to be functional and recognizable,
after further scrutiny, turn out to carry several interfering
(overlapping) messages simultaneously. This paper describes
various kinds of these interfering contexts and upgrades some
of them to the category of 'codes' of which the classical
triplet code is one veteran representative.
There is something else in the same sequence
Do we get right what the consensus sequences tell?
Common belief is that having enough versions of a given type
of the recognition sequence, one can derive a consensus with
a typical or even exemplary performance as the recognition
site. Surprisingly, however, there are several hard-to-believe
cases when some rather different sequences turn out to
perform better than the consensus.
Two such examples emerged from experiments with
randomization of the recognition sequences in a search for
a better one. In both cases, some of the sequences selected
from random sets were found to be superior in some
important aspects as compared to well-established consensus
sequences. The tet promoter of pBR322, TTGACA
TTTAAT, with an almost ideal TATA box and
ideal -35 sequence, was replaced by the sequence
TTTCTT
TAAGCT with only marginal resemblance to the wild type; yet 8-fold more mRNA was produced
with this artificial 'promoter' (Horwitz and Loeb, 1988).
Obviously, the promoter consensus reflects not only sequence
requirements for good mRNA production, but also some
other features that it is important for natural promoters to
have. In other words, the consensus tells something else, not
just what our limited knowledge about promoter structure
would suggest. Another case like that is a study on RNA
ligands of T4 DNA polymerase (Tuerk and Gold, 1990). The
consensus ligand AAUAACUC was, indeed, fished out from
the random sequences as one of the best performers, but not
the best. The sequence AGCAACCU showed, actually, even
stronger binding, although it was only a 50% match to the
natural consensus. Again, it appears that the consensus sequence
is not only about the binding. It has to satisfy some other sequence
requirements escaping our straightforward understanding.
All this suggests that reading the sequences in only one
way, i.e. expecting one message only, is generally wrong. Not
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E.N.Trifonov
infrequently some other messages hide both in the consensus
and in the contexts of the individual sites. This is not unlike
the following analogy (not to be taken as more than that),
where the 'messages' have even no connection with their
formal consensus. The words COOKY, MANGO, MELON,
HONEY, SWEET all suggest something sweet or sweet-sour
and could be considered, thus, as recognition sequences for
the 'sweet' quality. Their consensus sequence, however,
conveys a rather different message: MONEY.
The third positions do have something to say
The third, degenerate, positions of the codons in the mRNA
are normally given an unimpressive role of nearly neutral
drifting. Indeed, if two very closely related gene sequences
are taken, the difference between them resides almost
exclusively in the third positions, leaving the encoded
amino acid sequences unchanged. However, there are several
observations indicating that the third position is not quite
neutral. For example, a certain selection pressure in favor of
local G + C composition is well documented (D'Onofrio and
Bernardi, 1992). Analysis of the occurrences of four bases in
the three codon positions of mRNA also indicates that the
distribution of bases in the almost 'silent' third position
displays a significant degree of species specificity (Zhang and
Chou, 1994). Yet another bias is the frequent preference for U
in the third positions, a reflection of the hidden mRNA
consensus (GCU)n presumably responsible for keeping the
ribosome in the correct reading frame (Lagunez-Otero and
Trifonov, 1992). In other words, the third positions, indeed,
seem to carry some functional load or rather several different
loads, not necessarily one at a time. The degeneracy of the
triplet code (alternative bases in the third positions and
alternative triplets for leucine, serine and arginine) is not
wasted. Below, some particularly striking examples of the
utilization of the degeneracy are described.
The largest subunit of RNA polymerase II contains a Cterminal domain with tandem repeats of the highly conserved
seven amino acid sequence YSPTSPS. In the corresponding
cDNA, all six possible codons for serine are used (Corden et
al, 1985). One would expect that if the selection pressure
were only exerted on the amino acid sequence, thus allowing
mutations of the genomic sequence to all possible triplets for
serine. Close inspection of the distribution of the AGY serine
triplets along the cDNA reveals, however, that the most
frequent distances between the AGY triplets are not 6, 9, 12,
15 or 21 bases as the amino acid sequence would suggest but,
very selectively, 21 (also 42 and 63) residues. In other words,
the AGY triplets appear periodically, with the period 21
bases. Obviously, this has nothing to do with the selection
pressure on the amino acid sequence. The AGY periodicity is
needed for whatever purpose by the DNA (or RNA)
sequence. Perhaps, this is some message carried by DNA
424
for itself, since the period 21 bases corresponds closely to two
helical repeats of DNA.
A similar example, but apparently with a different type of
DNA level message, is provided by the sequence of one of the
MHC genes (Levi-Strauss et al, 1988). Here as well the
region 725-880 of the sequence codes for a repeating amino
acid sequence with strictly alternating positively and negatively charged amino acids, R, K and E, D, respectively. Four
different triplets for arginine are present in this sequence:
CGA, CGG, AGA and AGG; and, again, the degeneracy of
these triplets and of the third positions of other triplets is
highly non-random. The positions of purine bases strictly
follow a consensus (GA)n, such that all AG dinucleotides
occupy only odd positions in the sequence (727 and other odd
coordinates), while the GA dinucleotides are placed in even
positions (728 and other even coordinates). Perhaps at some
point in evolution this sequence was just an alternation of A
and G, and this alternation is still kept wherever the
degeneracy of the triplet code allows this. Why the twobase periodical alternation of A and G is almost as important
as six-base periodical alternation of triplets for positively and
negatively charged amino acids remains to be seen. Interestingly, the presumed ancestral repeating sequence (AGAGAG)n
coding for the repeat (RE)n would ideally satisfy both
alternations. Two other charged amino acids, K and D,
apparently, had to be introduced, compromising the original
simple pattern, but perhaps bringing in some important
advantage.
Another very interesting example is hypervariability of the
V3 region of the HIV-1 envelope gene. Here the changes
occur in the first two positions of the codons, while the third
positions are conserved (!): of 11 documented point changes
in this region, only one hits the third position (J.Hirshon and
J.Goudsmit, personal communication). This conservation,
obviously, is unrelated to the encoded protein and keeps
intact some additional message. It remains to be found what
that message means.
Codes other than triplet code
The code is any pattern or bias in the sequence which
corresponds to one or another specific biological (biomolecular)
function or interaction (Trifonov, 1989). The codes could be
specific or general, depending on the respective functions.
The sequence of a regulatory site operating with only a
limited group of genes or common to only a few species
would be an example of a specific code. Many transcription
factor binding sites are of this category. The universal triplet
code and, say, signals for transcription initiation and
termination are examples of general codes common either
to all species or at least to large kingdoms. This section is a
brief overview of current knowledge about various general
codes starting with those known sequence patterns which are
Interacting sequence contexts
normally not considered as codes, although they are
according to the above definition. The following codes are
very briefly described below: transcription codes, gene
splicing code, translation pausing code, DNA structure
codes, chromatin code, translation framing code, modulation
(adaptation) code and gene segmentation code.
Transcription codes include promoters and terminators,
and are rather universal though different in prokaryotes and in
eukaryotes. Most of the promoter sequences include the
TATA box or a close version of it (Pribnow, 1975; Goldberg,
1979). Prokaryotic promoter sequences are rather thoroughly
characterized and the algorithms are available to recognize
them in the sequences (e.g. Alexandrov and Mironov, 1990;
Demeler and Zhou, 1991). Prokaryotic terminators are
characterized as well (Brendel et ai, 1986; d'Aubenton
Carafa et ai, 1990; Yager and von Hippel, 1991). Eukaryotic
promoters are well reviewed (Bucher and Trifonov, 1986;
Bucher, 1996) and several algorithms to locate them in the
sequences have been attempted [Prestige (1995) and
references therein].
The gene splicing code for the processing of nuclear premRNA is largely undeciphered. Its main components are
obligatory GU- and AG-ends of introns (Breathnach and
Chambon, 1981), as well as rather conserved consensus
sequences and other sequence features around the ends
(Mount, 1982; Soloviev et ai, 1994). This information,
unfortunately, is insufficient to locate the splice sites in the
genomic sequences unequivocally. Apparent high precision
of the gene splicing would suggest that there are some other
important features of the excised and spliced sequences,
which remain to be revealed in future studies.
Translation pausing important for the regulation of
translation is encoded by clusters of rare triplets for which
the respective aminoacyl-tRNAs are in limited supply
(Varenne and Lazdunski, 1986; Kurland, 1991). A higher
potential of hairpin formation immediately downstream from
the rare triplets (Shpaer, 1985) also contributes to the
translation pausing pattern, providing a transient obstacle
for the ribosome. Since the rare codons are also sites of more
frequent translational errors, the translation pausing code may
also be called a 'fidelity code' (Shpaer, 1985).
The last two decades of studies on DNA structure revealed
that DNA is not a monotonous double helix all the same for
whatever sequence of base pairs is hidden in its interior.
Rather, it is a spectrum of local variations of the shape
dictated by the nucleotide sequence. In particular, the
molecule is frequently curved, which was originally revealed
from the DNA sequence analysis showing some dinucleotides
reappearing with the DNA helical repeat (Trifonov, 1980;
Trifonov and Sussman, 1980). The sequence-dependent local
shape of DNA is a crucial component of the protein-DNA
recognition. The corresponding sequence code (DNA shape
code) is universal and can be expressed in the form of a set of
26 angular parameters characterizing geometries of all 10
possible base pair stacks in DNA. The angles—wedge roll,
wedge tilt, and twist—have been estimated by computational
fitting to results of solution experiments with many DNA
fragments of different nucleotide sequences (Kabsch et ai,
1982; Bolshoy et ai, 1991). A computer algorithm exists
which calculates the DNA shape from its nucleotide sequence
based on the above 26 angles (Shpigelman et ai, 1993). There
are also several alternative models describing the sequencedependent DNA shape [Dickerson et ai (1996) and references
therein]. These models, however, predict DNA curvatures with
average misfit >2 SD away from the experimental estimates
(Bolshoy et ai, 1991).
A gross change in DNA helical shape is caused by
transition from one DNA form to another. Each form can be
characterized by A-, B- or Z-propensity energies calculated
for all trinucleotides (Basham et ai, 1995). This constitutes
DNA form codes for all three basic DNA helical structures.
Chromatin code describes those sequence features that
direct the histone octamer's binding to DNA and formation of
the nucleosomes. The pattern reflects deformational anisotropy of DNA (Trifonov, 1980), and consists primarily of
periodically alternating AA and TT dinucleotides with the
period ~10.4 bases (Mengeritsky and Trifonov, 1983;
Ioshikhes et ai, 1992). The latest analysis (Ioshikhes et ai,
1996) of compiled experimentally mapped nucleosome DNA
sequences (Ioshikhes and Trifonov, 1993) confirmed earlier
estimates of the phase shift between AA and TT dinucleotides
in the nucleosomal pattern (~5 bases), and allowed the
location of the AA dinucleotides preferentially on the histone
octamer surface (Ioshikhes et ai, 1996). This places the
complementary TT dinucleotides on the outer face of the
nucleosomal DNA, in accordance with UV-irradiation
experiments (Gale et ai, 1987; Pehrson, 1989). An alternative
nucleosome DNA pattern is also suggested where the
periodical AA and TT dinucleotides are in-phase (Satchwell
et ai, 1986). An important part of the chromatin code is
information about orientations of neighboring nucleosomes in
space indispensable for the description of local architecture
of the chromatin fiber. This information is, presumably,
expressed in the lengths of the linkers connecting the
nucleosomes (Noll et ai, 1980; Ulanovsky and Trifonov,
1986).
Overlapping with the triplet code is the translation framing
code (Trifonov, 1987). The correct reading frame during
translation is, apparently, secured by complementarity of the
'consensus' mRNA pattern, (Gcu)n (Trifonov, 1987; LagunezOtero and Trifonov, 1992), with the (Cxx)n mRNA contact
sites in the small subunit ribosomal RNA (Trifonov, 1987;
McCarthy and Brimacombe, 1994). The frame monitoring by
this scheme is reflected in several known cases of translational frameshifting where the change in the (Gcu)n frame,
apparently, causes the jump of the ribosome to the new frame
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E.N.Trifonov
(Trifonov, 1987). The presence of the G-periodical pattern in
the protein-coding sequences should be important, especially
for long polypeptide chains, to make the probability of the
ribosome jumping sufficiently low so that the translation
would faithfully proceed to the completion of the synthesis of
the entire long chain.
The most conspicuous of all sequence types are tandemly
repeating sequences, especially simple repeats. At first sight,
the tandem repeats seem to be meaningless and dispensable
since the copy numbers of the repeats are highly variable.
Analysis of the literature on the occurrences and functional
involvement of the repeats leads to the conclusion that the
very copy number matters no less than the repeat sequence
itself (Trifonov, 1989, 1990). It is suggested that the variable
copy number of a repeat serves as an adjustable variable to
modulate expression of the nearby gene or other biological
(biomolecular) function influenced by the repeats—modulation code (Trifonov, 1989, 1990). The particular mechanism
of the influence, which usually becomes a matter of major
interest, is actually irrelevant as soon as the influence is
sensitive to the tuner—copy number of the repeats.
Numerous 'triplet expansion' diseases [Perutz et al. (1994)
and references therein] are a good illustration of tuners that
went beyond their normal limits. The number three of the
repeat unit size is not, of course, a magic number, and one
would expect that 'expansion' diseases exist as well with
other repeat sizes. For example, the alcoholic syndrome in
mice is accompanied by a change in the copy number of the
(RY)n repeat sequence in the first intron of the alcohol
dehydrogenase gene (Zhang et al, 1987). It is speculated that
the tuning mechanism—fast changes in the copy numbers of
the tuning tandem repeats—also participates in the fast
adaptation to a changing environment (Trifonov, 1989,1990).
Indeed, the unequal crossing-over and replication slippage on
the repeating sequences, both causing changes in the copy
numbers, are rather frequent events compared to rare point
mutational changes in the protein-coding sequences. The
given gene's activity could be changed much faster by the
tuning mechanism than by the point mutations. The modulation code as described above could thus also be considered
as an adaptation code.
One of the emerging new codes is the genome segmentation code, i.e. the genomes appear to be built of rather
standard size units of an unknown number of types fused
together in various orders, very much like shuffled packs of
cards. The first evidence comes from the size distributions of
the natural polypeptide chains. Eukaryotic proteins, for
example, show preference for the size 123 amino acid
residues and multiples of this unit size (Berman et al, 1994).
Such a distribution could result from many acts of combinatorial fusion of different small genes, all of the same unit
size, at some early stage of evolution (Trifonov, 1995). One
prediction would be that at the points of fusion, i.e. every
426
120-125 residues, there would be some excess of methionines, formerly initiation residues of the fused small genes.
This expectation is well met, indeed (Kolker and Trifonov,
1995), although most of these boundary methionines
disappeared, apparently, due to mutations. The corresponding
DNA unit size, ~370 bp, is also observed in the size
distributions of DNA mobile elements (E.N.Trifonov, in
preparation), in particular, in the sizes of extrachromosomal
circular DNA (Gaubatz, 1990). What the sequence features of
the 'genome units' or the fusion sites are remains to be found.
Undoubtedly, there are many more codes hidden in the
sequences. Some of them could only be guessed, some
already disclose themselves by one or another sequence
feature. For example, the G+C content of isochores seems to
be maintained largely by the composition bias in third
positions of the triplets—'genomic code' (D'Onofrio and
Bernardi, 1992). There has to be some code responsible for
folding of mRNA and heterogeneous nuclear RNA. Surely,
the code should involve sequence complementarity, perfect
or imperfect (Trifonov, 1985), and, perhaps, mirror symmetry
elements presumably located in the loop regions—'RNA
loop code' (Beckmann et al, 1986; Trifonov, 1988). There
should be some pattern in the genomic sequences responsible
for mRNA editing [Maslov et al. (1994) and references
therein] and many more, including those which correspond to
function not even yet discovered.
Superposition and interaction of the codes
One of the most remarkable properties of genetic sequences is
that the codes they carry overlap, i.e. a given base in a given
nucleotide sequence may well belong to two or more different
codes simultaneously. This phenomenon, unique for the
genetic sequences, was predicted in 1968 by Holliday who
suggested, in particular, that the signals responsible for
recombination may well reside within protein-coding
sequences. The general idea was developed further in later
works (Schaap, 1971; Zuckerkandl, 1976a,b; Trifonov, 1981)
and soon confirmed by numerous experimental data, as
reviewed by Normark et al. (1983). The idea on the
multiplicity of the codes and their overlapping also appeared
in later independent studies (Caporale, 1984; Kypr, 1986;
Koningsef a/., 1987).
Essentially, the genetic sequences appear as texts designed
for several different reading devices, each one seeing in it a
message of its own kind. This is possible, of course, primarily
due to degeneracy of the superimposed codes which allows a
given letter (base, amino acid) serving a given message to be
replaced by another letter. As a result, some other message is
also satisfied, written by a different code. Such a design is
most natural in terms of selection: if, for example, in a given
sequence after some mutational changes a certain pattern
emerged which causes some useful interaction (protein-DNA,
Interacting sequence contexts
protein-RNA, protein-protein), then this pattern would
become a subject of positive selection pressure, together
with the whole sequence context with the patterns selected
earlier. Or if the sequence responsible for one function
happened to be good for something else, it would serve both
functions simultaneously, and there is no reason why it should
not. A good descriptive term for such sequence behavior is
'molecular opportunism', used by Doolittle (1988) for one of
the most striking examples of overlapping codes: crystallins,
eye lens proteins, serve as very specific enzymes in different
tissues of the same species, which have nothing to do with
their physical and optical properties suitable for the eye lens.
A theoretical treatment of the multicode information
systems with overlapping is not available. Such systems
have no precedent either in information theory or in theories
on codes. The main issue here is, perhaps, the relationship
between the number of codes present in the sequence and
the code degeneracies. The overlapping codes have to be
degenerate to allow the adjustment of the superimposed
sequence patterns to a common sequence which, thus,
represents all the interacting codes simultaneously. The
number of different codes overlapping in the given sequence
should be a function of their degeneracy: fewer codes of
low degeneracy and more codes of high degeneracy. Lowdegeneracy codes, obviously, would leave only a little
possibility for compromise, and the sequence would not,
thus, accommodate much of other codes. The superposition of
the codes should enrich the vocabulary of the sequences, i.e.
to make a broader spectrum of the oligonucleotides
(oligopeptides) present. Indeed, the combined vocabularies
of sufficiently different overlapping codes are certainly richer
than the component vocabularies. On the other hand, the
condition of compromise would force the interacting codes to
use all the wealth of their 'linguistic' arsenals, again making
the final vocabulary rich. The richness of the vocabulary can
be expressed as the sequence complexity, which can be
defined in several different ways (Konopka, 1990; Trifonov,
1990). Contrary to the complexity approach, classical measures
of information based on statistical non-uniformity of the
sequences are not applicable to the multicode systems. Indeed,
a highly complex sequence which carries many overlapping
codes is as far off the statistical mean as a simple repeating
sequence with only one code in it.
The linguistic consideration finds some confirmation in the
analysis of the complexity of protein-coding and non-coding
sequences (Konopka, 1990; Trifonov, 1990), and in complexity comparisons of protein sequences with human texts
(Popov et al., 1996). In particular, the prokaryotic sequences,
basically open for all kinds of interaction at all three levels
(DNA, RNA and protein), would, perhaps, carry many
overlapping sequence patterns all over their genomes,
including the rather conserved protein-coding regions [as
originally suggested by Holliday (1968)]. These regions
would be closer to the exhaustion of their capacity to
accommodate additional codes and should, therefore, have
higher linguistic complexity as compared to adjacent 'noncoding' sequences. Indeed, this is found to be the case
(Konopka, 1990; Trifonov, 1990). The human texts are
relevant in the context of sequence complexity because they
provide the only example available of the sequences carrying
only one code (read in only one way, consecutively letter by
letter). Such an example cannot be taken from biological
sequences, since they all are likely to carry many overlapping
messages. Comparison of the sequence complexities of the
human texts and of natural protein sequences, both in 20letter alphabets, shows that as one would expect human texts
are simpler (Popov et al., 1996). This, however, can only be
used as an illustration of the point on higher complexity of
multicode sequences, rather than proof, because the relative
simplicity of the human texts may also reflect imperfection of
human languages at this stage of their evolution.
The interaction between the codes can be studied by
singling out two interacting codes of interest and analyzing
the changes in the corresponding sequence patterns which are
caused by the superposition. One example of such a study is
known: analysis of the interaction of the gene splicing code
with the triplet code (Gelfand, 1992). It is found that the
codons flanking the splice junctions are biased to satisfy the
sequence requirements of the splicing signals. Interestingly,
the bias is different depending on the frame of the interruption
by the intervening sequence.
One more study case is interaction of the gene splicing
code with the chromatin code. The interaction is not yet
described at the sequence level, but well demonstrated by
other means. The connection chromatin-gene splicing has
been originally suggested as the very essence of the phenomenon of gene splicing (Zuckerkandl, 1981). In the terminology of the interacting codes, the suggestion was that the
intervening sequences had to be introduced to separate two
otherwise interfering messages: triplet code and chromatin
code. The exons were given primarily the protein-coding
function, while the introns were loaded by the sequence
requirements of the chromatin structure. The connection
splicing-chromatin soon found numerous supporting evidence. The typical size of the intron-exon pairs was found to
be ~205 bp which is close to the nucleosome 'repeat' size
(Beckmann and Trifonov, 1991). The linguistic sequence
complexity of exons turned out to be lower than that for
prokaryotic protein-coding sequences (Konopka and Owens,
1990; Trifonov, 1991), as one would expect, indeed, in the
case of spatial separation of the interacting messages in
eukaryotes. The introns show higher affinity to the nucleosomes (Soloviev and Kolchanov, 1986; D.Denisov et al, in
preparation) and the splice junctions are preferentially located
in the central positions of the nucleosomes (D.Denisov et al, in
preparation). Finally, experimental studies clearly demonstrate
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E.N.Trifonov
that the chromatin reconstituted on cDNA (without introns)
has no unique structure, contrary to the chromatin reconstituted on the genomic DNA (with introns) (Liu et al., 1995).
Yet another case of apparent interaction of different
sequence patterns is periodic positional distribution of
transcription factor binding sites CCAAT and GGGCGG in
eukaryotic promoters (P.Bucher and E.N.Trifonov, in preparation). Since the period observed, 10.2 ± 0.2 bases, is
close to the sequence period of the nucleosome DNA
(loshikhes et al., 1992; Ioshikhes et al, 1996), the periodicity,
quite likely, reflects the presence of the nucleosome
immediately upstream from the TATA box. The binding
sites, then, would be preferentially oriented in a specific way
relative to the surface of the histone octamer, e.g. to favor (or
disfavor) accessibility of the site for interaction with the
transcription factor.
Concluding remarks
The regulatory sequence elements and transcription signals,
in particular, should not be torn out of their contexts. The
sequence they are located in carries numerous messages both
related and unrelated to the transcription function. The choice
of the sequence elements belonging to a given binding site is
a compromise between several overlapping codes specific
to this sequence region. The consensus sequence of the
transcription signal is only a first approximation in the
description of the signal. A more appropriate, although still
approximate, description would be a matrix presentation in
which preferences of various signal elements (mono-, di-,
trinucleotides) for different positions along the sequence are
listed (Trifonov, 1983). In most cases, none of the elements
individually is necessary or sufficient for the recognition,
very much like individual voters in elections. The recognition
is distributional (Trifonov, 1983), such that the decision is
made after the weights of different signal elements present in
a given site are somehow combined. The distribution of
various signal components along the sequence and the use of
alternative components at the same position reflects the
multiplicity of the codes overlapping with the given
recognition sequence and the compromise, yielding to one
another in order to co-exist, as much as their degeneracies
allow.
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