P. falciparum, the main causal agent of human malaria. In this context, fitting HMMs to the specificities of the target proteome can help identify additional domains.Hidden Markov Models (HMMs) are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam, each domain family is represented by a curated multiple sequence alignment from which a profile HMM is built. In spite of their high specificity, HMMs may lack sensitivity when searching for domains in divergent organisms. This is particularly the case for species with a biased amino-acid composition, such as P. falciparum as an example, we compare approaches that have been proposed for this problem, and present two alternative methods. Because previous attempts strongly rely on known domain occurrences in the target species or its close relatives, they mainly improve the detection of domains which belong to already identified families. Our methods learn global correction rules that adjust amino-acid distributions associated with the match states of HMMs. These rules are applied to all match states of the whole HMM library, thus enabling the detection of domains from previously absent families. Additionally, we propose a procedure to estimate the proportion of false positives among the newly discovered domains. Starting with the Pfam standard library, we build several new libraries with the different HMM-fitting approaches. These libraries are first used to detect new domain occurrences with low E-values. Second, by applying the Co-Occurrence Domain Discovery (CODD) procedure we have recently proposed, the libraries are further used to identify likely occurrences among potential domains with higher E-values.Using P. falciparum proteome and the Apicomplexa phylum, and identify many domains that are not detected by previous approaches. In terms of the number of new discovered domains, the new approaches outperform the previous ones when no close species are available or when they are used to identify likely occurrences among potential domains with high E-values. All predictions on P. falciparum have been integrated into a dedicated website which pools all known/new annotations of protein domains and functions for this organism. A software implementing the two proposed approaches is available at the same address: http://www.lirmm.fr/∼terrapon/HMMﬁt/We show that the new approaches allow identification of several domain families previously absent in the  Protein domains are sequential and structural motifs that are found independently in different proteins, in different combinations. As such, domains seem to be functional subunits of proteins above the raw amino acid sequence level . SeveralThe Pfam database version 23.0) offers a collection of 10 340 HMMs/domains, which cover over 73% of all proteins in the Uniprot database . The Int.0 offersgathering threshold and is manually curated to ensure few false positives among detected domains where xi is the probability to emit the amino acid i in state x.Both previous approaches have the same drawback: only domain families already known in the target or its close relatives may benefit from detection improvement. We propose to circumvent this limitation by learning global correction rules that are applied to match states of all HMMs, in order to build a new and complete adapted library. We focus on match states because they model the conserved positions of the domains and thus bring most of the information contained in the alignments. The challenge is to derive rules that simulate the drift that separates the target organism sequences from the sequences used to train the HMM, while preserving as much position-specific information of the HMMs as possible. We propose two approaches for this, that are described below. In the following, e.g. JTT [t. These matrices are associated with a stationary distribution of amino acids; when t is large enough, the probability of any amino acid tends towards its stationary probability, regardless of the initial probability distribution. Here, we use this property to move the amino-acid distribution of match states toward the distribution of the target species, thus simulating the evolution of conserved positions under the constraints characteristic of this species.Substitution matrices  be the rate matrix and P(t)=(pij(t)) be the probability matrix associated with t, where i and j denote two amino acids and ij the change from i to j. We use a general time reversible (GTR) model, and thus Q is composed of two parts: the stationary amino-acid distribution π=(Πi) and the symmetric exchangeability matrix R=(rij), using equality: qij=rijΠj (i≠j). The diagonal elements are such that the row sums are all zero. Q is normalized, that is t of 1 corresponds to an expected number of substitutions per site equal to 1. Here we use the R matrix of the LG model [πdistribution computed from P. falciparum proteins (see below for details). P(t) is obtained by matrix exponentiation: P(t)=eQt.Let LG model , which rt) is built by applying P(t) to the probability distributions of all match states  using equation: x∗=x·P(t), where x is the original distribution vector of the match state, x∗ the new one, and both are row vectors. The evolutionary time t has to be carefully chosen: if too large, then all x∗ distributions tend toward π and are uninformative; if too small, then x∗∼ xand the correction has no effect. Several values ranging from 0.01 to 0.5 were tested in our experiments (see below).The new HMM library  K, and outputs a partition of Xi.e. each state x∈X is associated with a unique class i.e.:In profile HMMs, match states model the amino-acid distribution of conserved positions. This distribution reflects the physical and chemical constraints associated with the position. Thus, positions with similar distribution are likely to undergo similar constraints. The principle of the second approach is to cluster the match states of HMMs according to their amino-acid distribution, and then to learn correction rules for each of the cluster of match state  is the amino-acid distribution associated with class cx. Note that while other clustering algorithms could be used, the K-means algorithm provides a fair tradeoff between run time (X involves around 470000 states to be clustered) and the quality of the results.where e. We want to estimate the FDR associated with i.e. the probability Pr for Evaluating and comparing HMM libraries is a difficult task. As explained above, the original Pfam library provides, with each HMM, a manually-curated score-threshold which ensures few false positives among the detected domains. One effect of the HMM-fitting approaches described above (with exception of the score-adjustment method) is to move the amino-acid composition of HMMs toward the composition of the target organism. Globally, this tends to increase the score of proteins of the organism for any HMM. Hence, the recommended thresholds cannot be safely used with the modified libraries. We propose here a simple procedure to estimate the proportion of false positives among the new domains identified with a particular library. i.e. that are observed in the same proteins a significantly higher than expected number of times. This is achieved with the Fisher’s exact test, to cope with potentially small sample sizes [C of Conditionally Dependent Pairs (CDP). Next, from the target-species proteins that possess both known and new domains, we build a list of (known-new) domain pairs L, by randomly associating each new domain with one of the already known domains of the same protein. We denote as (dkdn) a pair of  domains of L. The list L is used to estimate the FDR of dn of L is globally the same as in all domains of L) is the same as in domains of mono-domain proteins (that are not in L). Although domains of mono- and multi-domain proteins are usually different, they globally share the same amino-acid composition, and there is no reason to believe that HMMs are more prone to false positives for either type.A well known tendency of protein domains is to appear preferentially with a few other favourite domains within a protein . We showle sizes . A p-valL| be the number of pairs in L. Now, we denote as L belongs to the set of CDPs C, given that the potential domain is a true positive; similarly, L belongs to C, given that the potential domain is a false positive. We can express the expected number of pairs in L that belong to C as Let |Thus, we have L| is known, and L that belong to C. For L′ is created by randomly permuting new domains of the pairs in L. This is equivalent to randomly permuting the new domains in the proteins of the target species, and thus simulates a situation where almost all new domains are likely false positives. L″pairs that are in C. The procedure is repeated several times and averaged to obtain a better estimate. For L′is created from all  domain pairs observed in proteins with at least two known domains. This simulates the situation where all new domains are true positives, and L′pairs that are in C.|P. falciparum proteome (see below), we do not observe this dependency, and standard thresholds between 10−2and 10−4give very similar results  is that the estimated value may depend on the p-value threshold used to build the set of CDPs. However, in experiments on the L in our FDR estimate. We address this issue with a classical bootstrap procedure [Lb is build by randomly sampling with replacement |L| pairs of L. From this list, we compute a new FDR estimate FDRbusing the procedure described above—i.e. we compute a new estimate of B . We then have a sample of B independent bootstrap replications of the FDR estimate, and we use the standard deviation of this sample as an estimate of the standard error.Another issue is the statistical error induced by the sample rocedure , in whici.e. Score adjustment, Enriched alignment, Substitution matrix and Match-state clustering—to the proteome of P. falciparum (PlasmoDB release 5.5), using the Pfam HMM library (version 23.0). There are a total of 5460 proteins in P. falciparum, 2900 of which have a known domain occurrence. In our substitution matrix approach, we must choose a stationary distribution. We tried two solutions for this. The first one corresponds to the average amino-acid distribution of P. falciparum proteins. However, P. falciparum proteins are known to exhibit low-complexity inserts with very biased amino-acid distribution which are responsible for an average increase of 20% in the total length of the proteins [t, which represents the mutation probability at each position, we tried seven values: 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, and 0.5. Finally, for the match-state clustering approach, different numbers of classes ranging from K=50 to K=500, as well as three mixture proportions  were tested.We applied the four approaches described above—proteins  to removP. falciparum proteins. In the following, we use the modified libraries for two problems. First, the aim is to identify new domains with low E-values; Second, the aim is to identify more divergent domain occurrences  with the help of the CODD procedure we have recently proposed [For each correction method and parameter value, a new HMM library is built. Each library is then calibrated with exception of the score-adjustment method derived library. Calibration is achieved by fitting the Gumbel distributions used for E-value computations to the new models (see HMMER 2.3.2 User’s Guide ). After proposed .0−3 to 10−1). For each library, the FDR associated with the newly identified domains was estimated with the procedure described above.The new libraries were first used to find new domains at low E-value ), the two Theileria species and Babesia bovis are in the Aconoidasida class (S2), the three Cryptosporidium species and T. gondii are in the Apicomplexa phylum (S3), and the kindom (Sm) incorporates all eukaryotes.In a first experiment, we used all known domains of available t=0.1. For match-state clustering, the curves suggest that 75% is the best mixture proportion. At low FDRs, the differences between the performance achieved by the different values of K are small, but K=100 seems to be the best. The investigated parameter values were chosen to span relatively large and a priori sound intervals. However, a more systematic exploration of the parameter space—especially for the time parameters t and the mixture proportion p—may slightly improve the results. However, we have chosen not to embark further on the parameter space exploration, as expected improvements are likely moderate, and as this allows for a fair comparison with the score-adjustment and enriched-alignment methods which both lack parameters. Figure Alveolata species. For comparison, we also show the results achieved with the original Pfam library at the same E-value thresholds (i.e. from 10−3 to 10−1). All methods discover several additional domains not identified with the standard library. The enriched alignment approach (blue curve) achieves the best results. Even if this approach cannot reconstruct all HMMs, the reconstructed models correspond to the most frequent families in the taxon. The match-state clustering approach (green curve) and substitution matrices (yellow curve) also identify additional domains. The weaker results obtained by the latter method may be due to the standard substitution schema that was employed. Indeed, exchangeability matrices model “universal” evolutionary mechanics, while P. falciparum is constrained by more extreme evolutionary circumstances. Moreover, this is the only approach that does not use information from close species. Finally, the score correction approach also discovers some domains not identified with the original Pfam library with the standard scoring function.The Additional file i.e. for all points in Figure vs. 283 at 10% FDR), and identifies slightly more domain families that were previously unknown in the Apicomplexa phylum (40 vs. 15).We also estimated the standard error of the FDR estimate using the bootstrap procedure previously described. Figure InterPro families that pool all representations of the same domain [To further assess the newly discovered domains, we next looked at the other InterPro domains that do not belong to the Pfam database. The InterPro database incorporates several domain databases  into a single resource. This meta-database organizes entries in P. falciparum and of the other available Alveolata species. To compare the performance of the methods depending on whether species close to the target were available or not, we conducted a second series of experiments using only known Pfam domains of the P. falciparum genome to fit the libraries . In these conditions, the results strongly differ from the previous ones. The number of new domains identified by the enriched alignment approach drops off, and even passes below the number of domains identified by the original Pfam library at low FDR. On the contrary, the match-state clustering approach still provides numerous additional domains. With the substitution-matrix approach, it appears to be the best approach when no close species are available. While for P. falciparum the practical interest is limited, this illustrates the potential of such approaches for all genome sequencing projects of organisms from poorly known phyla, with no already sequenced close-species.Apart from the substitution-matrix method, all approaches make use of the already known domains of i.e. a pair of the CDP set). The domains selected this way are said to be certified. The certification can be done on the basis of the already known Pfam domains of the protein, but also on the basis of the other known InterPro (non Pfam) domains, or even on the basis of the other new Pfam domains that are below the E-value threshold. Moreover, CODD uses a shuffling procedure to provide its own estimate of the FDR associated with the certified domains [0−1). As CODD also certifies the presence of domains with low E-values, part of the certified domains are already considered in the previous section. However, note that CODD is obviously limited to the certification of domains from multi-domain proteins, and thus that a large part of the previously considered domains (around 75%) cannot be certified. Moreover, it is worth noting that, contrary to the previous FDR, which involves all domains below a given E-value threshold, the FDR estimated by CODD only concerns the certified domains.We next investigated the performance of the modified libraries for identifying divergent domain occurrences using the CODD procedure we have recently proposed . CODD im domains . Because0−1 to 10, and the already known Pfam domains were used for the certification. In this experiment, the score adjustment, enriched alignment and match-state clustering approaches make use of the known domain occurrences of all Alveolata. The results differ somewhat from those achieved at low E-value in the same conditions. The enriched alignment and match-state clustering approaches now achieve similar sensitivity, while the score-adjustment approach does not detect more new domains than the standard Pfam library. This can be explained by the fact that the high E-value domains identified by co-occurrence are often uncommon among the already known domains of the species [Figure  species ,23. Thisi.e. the one obtained when removing low-complexity regions from P. falciparum proteins—is closer to the classical equilibrium amino-acid frequencies  than thi.e. Pfam and non-Pfam) as well as with the other new domain occurrences below the considered threshold.We also applied a bootstrap procedure similar to that described above to estimate the standard error associated with the CODD FDR. Irrespective of the library, the standard error remains quite low, ranging from 0.5% for the highest FDRs to 1.5% for the smallest ones (data not shown). Table http://www.lirmm.fr/∼terrapon/HMMﬁt/. Altogether, single domains and domain combinations improve the annotations of several P. falciparum proteins. Table i.e.∼6% of the 5791 already known GO annotations of this organism. All predictions on P. falciparum achieved using the Alveolata species have been integrated into a dedicated website which gathers all known and newly discovered domains and GO annotations for the proteins of this organism. The site provides details on E-values, FDRs and alignments, and includes useful links to Pfam, InterPro and PlasmoDB websites. Moreover, when a new domain has been certified with CODD, this information, along with details of the certification process, are included on the website.Any item or feature that would help assigning a function to a gene is precious for biologists. We thus investigated GO annotations that could be deduced from all newly identified domains. As described in the introduction, some domains have been associated with specific GO terms by the InterPro consortium. The policy is to associate, with a given domain, annotations shared by all annotated proteins possessing this domain. Moreover, by extending this policy to domain combinations (as described in ), severai.e. known domains, known functional annotations, protein attributes, etc.), and by carefully looking at the protein alignments.Although the expected number of false positives in predictions with 10% FDR is low, it is not equal to zero. Hence, an expert examination of the predictions is recommended to identify the 10% false positives. This can be done in several ways: by looking at the predictions shared with the other corrected libraries, by checking if a prediction agrees with known protein annotations .Regarding the control of gene expression, it is now established that an important general process in this parasite involves the control of chromatin condensation via chemical modifications of histones and/or of DNA bases . PFD0840in silico analyses of the malaria genome  and exp example ), suggesRegarding the biogenesis of intracellular organelles, the vital importance of the apicoplast has stimulated numerous studies geared at functionally dissecting the import and maturation of nuclear encoded proteins (for review see ). PF14_0http://www.lirmm.fr/∼terrapon/HMMﬁt/. Our methods learn global correction rules that are applied to match states of the entire library, thus enabling the discovery of domains that were previously unknown in a given organism. The two methods concentrate on modification of the amino-acid distribution of match states. This is because match-states model conserved positions of the proteins, and thus likely contain most of the information of the sequence alignments. However, several other solutions can be explored, such as modifying the number of states, the probabilities of between-state transitions, the amino-acid distributions associated with insertion states, or the null model used for computing scores and E-values.We have proposed two new methods to fit an HMM library to a target organism. These methods have been implemented in a software freely available at P. falciparum as a case study, we assessed the performances of our methods and of two previous approaches that rely on a specific adjustment of the score function, or on the enrichment of sequence alignments. Note that several other approaches address related issues. For example, Kumar and Cowen [Additionally, we have presented a simple procedure to estimate the proportion of false positives in a set of newly identified domains. Using nd Cowen  propose nd Cowen -32). HowP. falciparum, our experiments show that when several close genomes are available, the approach which integrates known domain occurrences into the sequence alignments provides the best results in terms of number of new occurrences. Although our approach based on match-state clustering identifies fewer new domains, it provides similar results in terms of domain diversity, and discovers slightly more new domain families. Moreover, when there is no closely related species available, our two approaches outperform the other methods in terms of occurrence number, domain diversity and number of previously unknown families. This is also the case when the modified libraries are used to detect divergent domain occurrences with the help of the CODD procedure.In P. falciparum several hundred domains that are not identified with the original Pfam library. At low E-value thresholds, the Pfam standard library identifies 192 new domains at 10% FDR, while the enriched alignment and match-state clustering libraries identify 486 and 375 new domains, respectively, at the same FDR. With the help of the CODD procedure, the original library identifies 404 new domains at 10% FDR, while the enriched alignment and match-state clustering libraries identify 529 and 516 new domains, respectively. Additionally, the newly identified domains often provide new GO annotations for P. falciparum proteins. For example, the enriched alignment and match-state clustering libraries lead to an additional 348 and 355 new GO annotations, respectively. All new domains discovered on P. falciparum have been integrated into a dedicated website which pools all known/new annotations of protein domains and functions for this organism.In summary, the fitted libraries identify in The authors declare that they have no competing interests.NT, OG and LB conceived and designed the method and experiments. NT implemented the approach. EM analysed the biological results. NT and LB drafted the manuscript. OG initiated the project. All authors have read and approved the final manuscript.Supplementary figures and tables. This PDF file contains four supplementary figures, and three supplementary tables.Click here for file


Ferroelectric vortex domain structure which exists in low-dimensional ferroelectrics is being intensively researched for future applications in functional nanodevices. Here we demonstrate that adjusting surface charge screening in combination with temperature can provide an efficient way to gain control of vortex domain structure in ferroelectric nanodot. Systematical simulating experiments have been conducted to reveal the stability and evolution mechanisms of domain structure in ferroelectric nanodot under various conditions, including processes of cooling-down/heating-up under different surface charge screening conditions, and increasing/decreasing surface charge screening at different temperatures. Fruitful phase diagrams as functions of surface screening and temperature are presented, together with evolution paths of various domain patterns. Calculations discover up to 25 different kinds of domain patterns and 22 typical evolution paths of phase transitions. The fruitful controllability of vortex domain structure by surface charge screening in combination with temperature should shed light on prospective nanodevice applications of low-dimensional ferroelectric nanostructures. Ferroelectric materials have received considerable attentions from both academics and industries owing to their broad applications in multifunctional electric devices, such as memories, actuators and sensors, etc123467891112131471617181920A common feature for ferroelectrics is the formation of domain structure as the material is cooled down through Curie temperature, due to the coexistence of different energetically equivalent polar domains. In general, stability and features of domain structure, e.g., domain configuration, domain size and domain wall thickness, depend on the delicate balance of the electromechanical energies that are sensitive to the boundary conditions, which is especially true for ferroelectric nanostructures. Particularly, recent researches have shown that low-dimensional ferroelectrics can form a special type of domain structure with closure polar domains, namely vortex domain structure (VDS)1131522232425262728It has been demonstrated that the domain structure in ferroelectrics could be dramatically changed by electric field, mechanical field and temperature, etc., which forms the base of domain and domain wall applications671931323334351314371931343536394034It is worth noting that surface charge screening should be also effective to influence VDS, considering the sensitivity of its formation on the electric boundary conditionIn this work, we perform phase-field simulations on ferroelectric nanodots under variable surface charge screening conditions. Based on the important effect of surface charge screening on the domain structure and the exigent need of obtaining transformation paths of domain structure, simulating experiments of nanodots under processes of increasing/decreasing charge screening are conducted. In addition, owing to the practical application of nanodevices in complicated environment, the effects of temperature variation  on the domain structure of nanodots are also investigated systematically. Fruitful domain patterns, transformations and phase diagrams are revealed and summarized.3 nanodot as a model system to demonstrate the effect of charge screening and temperature on the VDS of the ferroelectric nanostructures. Some necessary information of bulk BaTiO3, i.e., phases, their temperature ranges, and polarization at selected temperatures, has been listed in 3 nanodot revealed in the following.Our investigation is organized into four main sections according to the important simulating experiments as schematically illustrated in z direction. When the nanodot is under ideal open-circuit boundary condition, the depolarization field would be the maximum owing to no polarization charge being compensated. As surface charge screening increases, the depolarization field would decrease and achieve the minimum under ideal short-circuit boundary condition. Basing on this fact, when the nanodot is under charge screening, we can approximate the extent of charge screening by introducing screening factors iβ , and the depolarization field in the nanodot is calculated by iβ vary from zero to one, the charge screening condition of the nanodot changes from the ideal short-circuit boundary condition (iβ = 0) to the ideal open-circuit boundary condition (iβ = 1) accordingly. In our investigation, the charge screening is at the top and bottom surfaces, i.e., β1 = β2 = 1, 0 ≤ β3 ≤ 1. To clearly characterize each domain pattern, we calculate its average polarization vector r is the position vector, V is the volume of the nanodot, and P is the spontaneous polarization vector. The average magnitude of polarization at all the sites The ferroelectric nanodot is supposed to be under charge screening at the top and bottom surfaces, i.e., axial screening along the β3 = 1) and near short-circuit condition (β3 = 0.2), respectively. For each cooling-down process, a random perturbation of polarization field is introduced to initiate the polarization evolution at high temperature. Once a stable domain pattern is formed below paraelectric-ferroelectric transition temperature, it is used as the initial domain pattern for the next temperature step. When cooled down under open-circuit boundary condition  indicates a strong size effect of the nanodot. For this domain pattern, the three components of the toroidal moment are almost equal with each other, i.e., |xg| ≈ |yg| ≈ |zg|. From the morphologies of equilibrium domain pattern in 273 with tetragonal, orthorhombic and rhombohedral ferroelectric phases, tetragonal and orthorhombic vortex domain patterns are suppressed in the nanodot under ideal open-circuit condition. Nevertheless, this may become different in case of charge screening conditions. Indeed, when the nanodot is cooled down under screening condition β3 = 0.2, i.e., with most top and bottom surface charges being compensated, a vortex domain pattern with vortex axis along <100> direction  forms between 245 K and 255 K as seen in xg| = |yg|, |zg| = 0) appears and keeps stable until T reaches 215 K. Then the nanodot changes into orthorhombic-like vortex domain pattern, whose z-component of toroidal moment is less than the other two components. Therefore, the electric boundary condition could largely affect the stability of VDS in the ferroelectric nanodot. Here we would like to point out that elastic energy and gradient energy also play important roles in affecting the domain stability besides electric energy. Actually, tetragonal, orthorhombic and rhombohedral domain patterns would emerge under ideal open-circuit condition if the elastic energy is artificially switched off. Meanwhile, tetragonal and rhombohedral phases would appear under ideal open-circuit condition in cooling-down process when the gradient energy coefficients are set as isotropy, e.g., G11 = 3.46 × 10−10 Jm3C−2, G12 = 0 Jm3C−2, G44 = 1.73 × 10−10 Jm3C−2. These results together indicate that the domain stability of ferroelectric nanodot is a result of complicated factors.First of all, we would like to see whether surface charge screening would affect the domain structure obviously. ondition , the nanT = 100 K. From x-y plane with the increasing of surface charge screening, which indicates the appearance of orthorhombic-like vortex domain pattern (|xg| ≈ |yg| > |zg|). In the process of β3 decrease from 0.16 to 0, the average polarization component The above calculation presents the effect of temperature on domain structure in a nanodot under fixed surface charge screening. To know how the domain structure would evolve when the surface charge screening changes, we further simulate a nanodot in process of increasing surface charge screening from ideal open-circuit condition to ideal short-circuit condition at In the following, we would like to make a comprehensive research on the evolution of domain pattern in nanodots during cooling-down process under various surface charge screening conditions. Similar with the simulation shown in β3 = 0.2 and β3 = 1, where the equilibrium domain patterns are all vortex domain patterns. To be specific, in the process of cooling-down under ideal open-circuit condition (β3 = 1) and near open-circuit (β3 = 0.6 ~ 0.9) boundary condition, the nanodot adopts rhombohedral and orthorhombic-like vortex domain patterns respectively, with the corresponding toroidal moment components and the average magnitudes of polarization gradually increasing. The typical transformation paths are shown in β3 = 1) and 4b (β3 = 0.7). Interestingly, when the charge screening factor β3 is between 0.3 and 0.5, the nanodot adopts a vortex domain pattern with opposite rotation directions at the top and bottom surfaces at T ≥ 240 K. At the conjunction of the two vortices, there are also four small vortices locating at middle of the edges of nanodot. As the nanodot is further cooled down, the domain pattern would change into three similar vortices in succession with |xg| ≠ 0 and β3 = 0.5  and β3 = 0.4 (T < 150 K). The detail of this evolution process is shown in zg| than the other two components, as can be seen in β3 = 0.4. During the cooling-down process at charge screening β3 = 0.2, the nanodot would first form a vortex domain pattern  with the dipoles lying in x-y plane as shown in xg| = |yg|,|zg| = 0) at 240 K  at high temperature. The toroidal axes of the vortices are along zg| ≠ 0 near 0 K, as depicted in β3 = 0.1) and 4g (β3 = 0.05). In this region, the toroidal moment components |xg| and |yg| of the domain patterns are always identical. They are null when β3 is between 0.06 and 0.1 and are nonzero when β3 = 0.05 below 230 K.In the second region of the phase diagram in β3 < 0.05, and its characteristic is no obvious dipole vortex in the domain patterns. Particularly, the nanodot would first adopt 180°-like domain pattern  during the cooling-down process (0.01 ≤ β3 ≤ 0.04), because the significant surface charge screening can largely reduce the depolarization field along z-direction. Then the domain pattern would transform into a tilted 180°-like domain pattern with only |yg| = 0 or |zg| = 0 at 0 K. This transformation path of domain pattern can be clearly seen in xg| ≠ 0 would evolve into the tilted 180°-like domain pattern with |yg| being null directly at β3 = 0.04. However, when the screening factor β3 is 0.02, the domain pattern would experience an intermediate state with a small |yg| shown in xg| = |yg| = |zg| = 0 and T = 80 K  with average polarization component P> increases gradually from zero when the temperature decreases through the paraelectric-ferroelectric transition point with screening factor β3 being large  that appears under condition β3 = 0.3 during heating-up process does not appear in the cooling-down process as shown in xg| ≠ 0, |y,zg| = 0 can transform into the orthorhombic-like vortex domain pattern in the cooling-down process as depicted in β3 is 0.02, the toroidal moment component |yg| of the domain patterns always keeps null with the temperature increasing. However, a 180°-like domain pattern with a small |yg| is formed in the cooling-down process with β3 being 0.02 (5 K < T < 100 K).From the temperature and charge screening phase diagram of heating-up process in β3 < 0.04, the difference of heating-up and cooling-down phase transition temperatures becomes significant, depicting an increasing first-order feature, and the maximum difference of phase transition temperature between these two processes would reach 20 K at β3 = 0. Compared with the heating-up and cooling-down phase diagrams of the domain patterns  but a curved line. More importantly, the domain pattern exhibits a net polarization, i.e., zg| at charge screening β3 ≤ 0.02. In this transformation process, toroidal moment components |xg| and |yg| are equal throughout the process of charge screening increasing. When β3 is less than ~0.2, the average polarization 2 under the ideal short-circuit boundary condition.In case of T = 100 K, the transformation behavior of domain pattern is similar with that at T = 50 K until charge screening β3 ≤ 0.05, in which toroidal moment component |zg| becomes zero , with the three toroidal moment components being null as shown in β3 = 0.03  is formed and shown in β3 from 0.2 to 1, the nanodot always keeps orthorhombic-like vortex domain pattern. The transformation of the domain patterns at 50 K is much similar to that at 0 K. However, the nanodot would form rhombohedral vortex domain pattern under open-circuit boundary condition, which is indicated in β3 in-between 0.1 and 0.13, another domain pattern with 90° domain walls and |x,y,zg| ≠ 0 appears (see When the temperature is 0 K, the initial single-domain-like pattern of the nanodot keeps stable until ears see , which iT = 150 K, the nanodot forms a polar vortex domain pattern (|xg| = |yg|, |zg| = 0, β3 increases from 0.08 to 0.13, with the cores of dipole vortices along a curved line and the dipole vortices parallel to the z-axis. From xg| and |yg| of this vortex domain pattern are equal with each other, and its average polarization component β3 = 0.13 to β3 = 0.2 (β3 increases to 0.06, and it becomes orthorhombic vortex domain pattern at β3 = 0.08. As β3 in-between 0.26 and 0.38, the nanodot adopts a vortex domain pattern along <100> direction with |xg| = |yg| = 0 shown in β3 = 0.41. Basing on these results, we can find that the 4-vortices domain pattern does not appear in this process, which is quite special and appears in increasing charge screening process at T = 250 K (see At β3 = 0.2 . As the 50 K see .In summary, we have systematically investigated the transformations of vortex domain structure in a ferroelectric nanodot under the control of surface charge screening and temperature. Through adjusting the electric boundary condition or temperature, we have shown that the nanodot can form fruitful vortex domain structures. Four significant charge screening and temperature phase diagrams are summarized, together with the typical evolution paths of domain patterns. It is found that the transformations of domain structures in the processes of heating-up and cooling-down under the same charge screening boundary condition are similar approximately. However, the phase diagrams under increasing and decreasing charge screening at the fixed temperatures are significantly different from each other, indicating that the initial domain structure plays an important role in these transformations. In addition, when the nanodot is under the near short-circuit boundary condition or at high temperature near paraelectric-ferroelectric phase transition temperature, more various domain patterns can appear due to the strong charge screening effect and small polarization. These results reveal the prospective application of charge screening and temperature in the design of nanoscale multifunctional devices.Our phase-field simulation of ferroelectric domain structure is based on a Landau-Devonshire-Ginzburg free-energy, which takes into account the effects of inhomogeneous electromechanical fields. Finite element methods are adopted to solve the electrostatic and mechanical equilibrium equations. The evolution of polarization field is solved by discretizing the time-dependent Ginzburg-Landau equation to determine kinetics of polarization field. In the calculations, we assume that the system reaches the electrostatic and mechanical equilibrium instantaneously once the spontaneous polarization distribution is changed. To get the effect of charge screening and temperature on the vortex domain structure of the ferroelectric nanodot, a meshing of 10 × 10 × 10 elements with interval scale being equal to 0.4 nm is used to simulate a cubic nanodot, and the charge screening is at the top and bottom surfaces. A detailed description of the phase-field approach is provided in the Y.Z. initiated and performed this work and manuscript. B.W. suggested the principle idea. W.J.C. and Y.Z. conceived and designed the basic idea, structures and simulations. C.M.W. and W.J.C. performed the simulations. Y.Z., C.M.W., W.J.C., B.W., D.C.M., C.H.W. and J.Y.L. analyzed the results of simulations. Y.Z., B.W., C.M.W. and W.J.C. co-wrote the manuscript. All authors contributed to discussion and reviewed the manuscript.Supplementary Information


Cookies were produced from germinated pigeon pea, fermented sorghum, and cocoyam flour (CF) blends to determine their potentials in cookie manufacture. Ten flour formulations were produced and they were evaluated for their proximate and functional properties. Protein content ranged from 4.85% to 19.89% with 100% CF (100CF) having the least value, while 100% germinated pigeon pea flour (100GPF) had the highest value. Increase in levels of GPF to the flour blends resulted in increase in protein content of the blends. Cookies made with 100% fermented sorghum flour (100FSF) had the highest ash content of 2.73%, while cookies made with 100GPF had the least ash content. Energy values of the cookies ranged between 369.37 and 376.56 kcal/100 g, with cookie formulation 50%CF:50%FSF having the least value and cookies made with 16.7%CF:16.7%FSF:66.6GPF having the highest value. The control (cookies made with wheat) had the highest spread ratio of 24.13, while cookies made with 100FSF had the least spread ratio of 14.97. Cookies made with 100CF were the least fragile. Sensory ratings revealed that cookies containing up to 50% CF and above, compared favorably with those made with wheat flour. The importance of this work arises from the possibility of utilizing flours from locally grown crops other than wheat to produce cookies of acceptable nutritional, sensory, and physical quality. The use of germinated pigeon pea, fermented sorghum, and cocoyam flour (CF) blends in cookie manufacture can greatly enhance the protein content, without compromising consumer acceptance.Cookies are snacks that are popular and widely consumed all over the world by people of all ages. They are traditionally made from soft wheat, a cereal, which is cultivated in many parts of the world, but imported by countries with unfavorable climatic conditions. Such importing countries spend a lot of foreign exchange on importation of wheat. There is a compelling need to develop an adequate substitute for wheat, as the demand and price of this product could further be increased by natural disasters such as hurricanes, which destroy wheat crops and also the fact that many farmers are beginning to switch from growing wheat to growing “more lucrative” crops (like corn and soy beans), which could be used in the production of biofuels. In the quest for a wheat substitute, flour with better nutritional quality than wheat would be highly desirable, especially in developing countries where malnutrition is prevalent.Composite flour can be defined as a mixture of several flours obtained from roots and tubers, cereal, legumes, etc., with or without the addition of wheat flour (Adeyemi and Ogazi Cajanus cajan), an important food legume, is mainly a subsistence crop in the tropics and sub-tropics of India, Africa, South-East Asia, and Central America , a cereal, is a member of the grass family. It is the fifth most important cereal in the world after wheat, rice, corn, and barley. More than 35% of sorghum is grown for human consumption, while the rest is primarily used for feed and industrial alcohol production  is an edible root crop grown in the tropics of which Nigeria is a major producer. It belongs to the family Aracea , the white variety of sorghum (S. bicolor), and the tannia variety of cocoyam (X. sagittifolium) were purchased from a retail outlet in Abakaliki, Ebonyi State, Nigeria. Wheat flour and all other baking ingredients such as eggs, baking powder, fat, milk, and flavorings were also obtained from the same source.The white variety of pigeon pea . Means were separated using Duncan's multiple range test using the Statistical Package for the Social Sciences (SPSS) version 16.0 .The proximate composition of the flour samples is shown in Table P < 0.05) protein content than the control (cookies made with wheat flour). Cookies made with 50CF:50FSF had higher carbohydrates content than cookies made with the composites. The control, however, had the highest carbohydrates content. The fat content of the cookies were generally low and this is likely to be desired by weight watchers. The fiber contents of all the cookies were within the recommended range of not more than 5 g dietary fiber per 100 g dry matter  were observed in the weight, thickness, diameter, and spread ratio of the control and some of the cookies made with the composite flours. Cookies from wheat had the highest spread ratio and it was not significantly different from three of the formulations. Cookies having higher spread ratios are considered the most desirable  between the ratings given to the control and cookies made with 100CF for all the sensory attributes. With the exception of general acceptability, cookies made with 50CF:50FSF and 66.6CF:16.7FSF:16.7GPF were not significantly different (P > 0.05) from the control. While the addition of CF improved ratings for the cookies, the addition of GPF had the opposite effect. All the cookies containing at least 50% GPF (with the exception of 16.7CF:16.7FSF:66.6GPF) received low scores (≤6) for texture, taste, crispness, and general acceptability. The results of the sensory analysis showed that texture data for cookies from 100GPF were in good agreement with the measurement derived from the fragility tests. Panelists described cookies containing high levels of GPF as having a bitter aftertaste and lacking the characteristic crispness and texture associated with cookies. This infers that even if cookies containing GPF have high protein content, many people may not be willing to eat them because of the low sensory quality. Except for those formulations containing above 50% GPF, all the cookies were at least slightly liked for the parameters studied.Table This study has shown that acceptable cookies of high-protein content can be produced from flour blends of cocoyam, germinated pigeon pea, and fermented sorghum. Several of the blends produced cookies that compared favorably with cookies from wheat flour (with respect to most of the sensory attributes). The use of these locally grown crops will go a long way in reducing dependence on wheat flour, thereby reducing foreign exchange used in importing wheat.


In view of the fact that appearance of novel protein domain architectures (DA) is closely associated with biological innovations, there is a growing interest in the genome-scale reconstruction of the evolutionary history of the domain architectures of multidomain proteins. In such analyses, however, it is usually ignored that a significant proportion of Metazoan sequences analyzed is mispredicted and that this may seriously affect the validity of the conclusions. To estimate the contribution of errors in gene prediction to differences in DA of predicted proteins, we have used the high quality manually curated UniProtKB/Swiss-Prot database as a reference. For genome-scale analysis of domain architectures of predicted proteins we focused on RefSeq, EnsEMBL and NCBI's GNOMON predicted sequences of Metazoan species with completely sequenced genomes. Comparison of the DA of UniProtKB/Swiss-Prot sequences of worm, fly, zebrafish, frog, chick, mouse, rat and orangutan with those of human Swiss-Prot entries have identified relatively few cases where orthologs had different DA, although the percentage with different DA increased with evolutionary distance. In contrast with this, comparison of the DA of human, orangutan, rat, mouse, chicken, frog, zebrafish, worm and fly RefSeq, EnsEMBL and NCBI's GNOMON predicted protein sequences with those of the corresponding/orthologous human Swiss-Prot entries identified a significantly higher proportion of domain architecture differences than in the case of the comparison of Swiss-Prot entries. Analysis of RefSeq, EnsEMBL and NCBI's GNOMON predicted protein sequences with DAs different from those of their Swiss-Prot orthologs confirmed that the higher rate of domain architecture differences is due to errors in gene prediction, the majority of which could be corrected with our FixPred protocol. We have also demonstrated that contamination of databases with incomplete, abnormal or mispredicted sequences introduces a bias in DA differences in as much as it increases the proportion of terminal over internal DA differences. Here we have shown that in the case of RefSeq, EnsEMBL and NCBI's GNOMON predicted protein sequences of Metazoan species, the contribution of gene prediction errors to domain architecture differences of orthologs is comparable to or greater than those due to true gene rearrangements. We have also demonstrated that domain architecture comparison may serve as a useful tool for the quality control of gene predictions and may thus guide the correction of sequence errors. Our findings caution that earlier genome-scale studies based on comparison of predicted (frequently mispredicted) protein sequences may have led to some erroneous conclusions about the evolution of novel domain architectures of multidomain proteins. A reassessment of the DA evolution of orthologous and paralogous proteins is presented in an accompanying paper . A survey of recent papers describing analyses of the evolutionary history of the DA of proteins, however, suggests that problems with each of these points may have had a strong impact on the validity of the conclusions.1.1.A general problem of studies on DA evolution is that true change of DA (at the genome level) may be confused with change of DA only at the transcript level, due to alternative splicing. As discussed in the present manuscript this type of problem is sometimes encountered even in the case of high-quality, manually curated Swiss-Prot section of UniProtKB; different isoforms (with different DA) are presented for orthologous genes with similar genomic structure.et al. [Although the Swiss-Prot section of UniProtKB contains only a few non-valid, incomplete or erroneous sequences, the TrEMBL section of UniProtKB is heavily contaminated with N-terminally or C-terminally truncated and chimeric sequences . In viewet al.  concludeTo estimate the contribution of sequence errors to differences in DA, in the present work we have compared data obtained on the high quality, manually curated Swiss-Prot database with data obtained on TrEMBL sequences of Metazoa. Our analyses have confirmed that DA differences due to errors of TrEMBL sequences may significantly exceed the rate of true DA changes.In the case of genome-scale analyses of DA changes, the majority of protein sequences analyzed is predicted: the accuracy of the predicted sequences depends on the type of genome and the performance of protocols used for the identification of protein-coding genes in genomic sequences. Protein-coding genes encoded by intron-poor genomes are usually predicted with great specificity and sensitivity. However, correct prediction of the genomic structure of the protein-coding genes of higher eukaryotes with intron-rich genomes is still a very difficult task. Recent analyses have shown that the exact genomic structure of protein-coding genes of higher eukaryotes is correctly predicted for only about 60% of the genes  the highIt must also be pointed out that computational gene prediction introduces a strong positional bias in the distribution of errors in as much as the initial and terminal exons of genes are predicted with significantly lower accuracy than internal exons . In termAlthough many authors realize that some of the DA differences result from errors of gene prediction, the contribution of this to DA differences has not been explored. To estimate the contribution of errors of gene prediction to differences in DA in the present work we have compared data obtained on the high quality, manually curated Swiss-Prot database with data obtained on databases containing less reliable, predicted sequences  of Metazoa.Our analyses have shown that DA differences due to errors in gene prediction may significantly exceed the rate of true DA change; therefore domain architecture comparison may serve as a tool for the quality control of gene predictions and may guide the correction of sequence errors. We have shown that application of this approach significantly improves the quality of gene predictions and promotes more reliable identification of true cases of domain architecture changes.Our finding, that errors in gene prediction significantly distort the patterns of DA evolution, cautions that earlier studies based on comparison of predicted (frequently mispredicted) protein sequences may have led to some erroneous conclusions about the evolution of novel domain architectures of multidomain proteins. The influence of gene prediction errors on DA evolution of orthologous and paralogous proteins is discussed in an accompanying paper .1.2.The choice of protocol for domain identification may have a strong influence on the specificity and sensitivity of domain architecture comparison. Since many domain-types defined by the Pfam database ,9 or theThe definition of ‘domain architecture’ of proteins also has a strong influence on the conclusions that can be drawn from comparison of proteins. A survey of the literature reveals that, although the majority of authors define DA as the linear sequence of constituent domains from the N-terminus to the C-terminus ,18,19, mIn some analyses, uninterrupted tandem repeats of the same domain-type are collapsed into a single pseudo-domain, therefore these analyses do not detect tandem duplication of a domain or deletion of a tandem duplicated domain ,26. To gAlthough it is clear that the choice of protocols and choice of cut-off values for domain identification have a major influence on DA comparison, very little is known about the sensitivity and specificity of the procedures used to detect differences in DA in the various studies. In the absence of this information it is not known to what extent the various approaches used in the different studies overestimate or underestimate DA differences.To overcome this problem, in the present work we optimized our DA comparison protocol using the high quality manually curated Swiss-Prot database as a benchmark. False positive rate and specificity of detection of DA differences were determined by comparing orthologous Swiss-Prot entries known to have identical domain architectures. False negative rate and sensitivity of detection of DA differences were determined using datasets of orthologous Swiss-Prot entries with artificially altered domain architectures.1.3.To estimate the contribution of different types of events to changes in domain architecture one has to correctly define the evolutionary relationship between the homologous multidomain proteins that are compared.Establishing the evolutionary relationship of multidoman proteins, however, is not trivial. First, since in many cases not all parts of two homologous multidomain proteins have the same evolutionary history, the usual terms for homology  of proteins may not apply. Considering these problems, it has been suggested that the use of the concept of orthology is applicable only at the level of domains rather than at the level of proteins, except for proteins with identical domain architectures ,28. The Despite these caveats, some authors analyzing DA changes have relied on trees determined for entire multidomain proteins ,34, but In the present work we have used sequence-based phylogeny of entire multidomain proteins and checked the reliability of orthology/paralogy assignments on representative samples of correctly annotated Swiss-Prot entries of multidomain proteins. Our results have shown that standard procedures used for establishing orthology are quite accurate even for multidomain proteins, but are much less reliable in defining groups of paralogs. The latter problem will be discussed in an accompanying paper .2.2.1.Comparison of DAs of Swiss-Prot proteins with those of artificially altered orthologs mimicking gene prediction errors  revealed that artificial chimeras mimicking fusion of neighboring genes were detected as differing from their parents/orthologs in domain architecture in the majority (98%) of the cases. In other words, the present procedure is quite sensitive for the detection of fusion of tandem genes that appear as DA changes of the terminal type.In the case of artificial  deletions of 100 amino acid residues (simulating gene prediction errors that miss some true exons but do not shift the reading frame) only 12% of the sequences are detected as having altered DA. The explanation for this relatively weak effect on DA, is that the deletion of 100 residues did not always affect a Pfam A domain and, even if it did, it rarely removed an entire Pfam A domain and the truncated Pfam A domain was still detected.In the case of artificial  additions of 100 residues taken from other proteins (mimicking the erroneous inclusion of true protein-coding exons) a high proportion (75%) of the artificial sequences had domain architectures different from those of their parents: the introduced fragment (that may have contained a Pfam A domain or a fragment of a Pfam A domain) was detected with our protocol.It seems likely that this asymmetry in the effect of erroneous omission or erroneous addition of a true exon (that does not cause reading frame shift) on DA has a significant impact on domain architecture comparisons. Since very few genes contain nested genes –40 that Addition of 100 residue-long random amino acid sequences at the termini of proteins  had no effect on the domain architectures, whereas internal insertion of such sequences  resulted in change of domain architecture in a relatively high proportion (25%) of the cases. The explanation for this observation is that internal insertion of random sequences have sometimes split Pfam A domains and this led either to failure in domain identification or virtual ‘duplication’ of the split domain. Nevertheless, erroneous inclusion of false exons will not introduce a positional bias in DA change, since domains may be affected irrespective of their internal or terminal position.So far we discussed only gene prediction errors that do not disrupt the reading frame. Mispredictions that result in reading frame-shift lead to truncation downstream of the point of such misprediction; irrespective of their position within the gene they lead to C-terminal truncation and will appear as C-terminal DA change.Similarly, indel-type of errors of cDNA sequences resulting from cloning or sequencing errors may cause reading frame shift (if the indel involves 3n + 1 or 3n + 2 nucleotides) and this will lead to truncation downstream of the point of such an error; irrespective of their position within the gene they lead to C-terminal truncation and might appear as C-terminal DA change. For example, a single base ‘deletion’ caused by such an error  resultedFinally, it should be pointed out that current computational gene prediction tools introduce a strong positional bias in the distribution of errors, in as much as the initial  exons are predicted with the lowest accuracy, terminal  exons of genes are predicted with somewhat greater accuracy, whereas internal exons are predicted with high accuracy . In term2.2.Homo sapiens, Pongo abelii, Mus musculus, Rattus norvegicus, Gallus gallus, Xenopus tropicalis, Danio rerio, Caenorhabditis elegans and Drosophila melanogaster.The manually curated Swiss-Prot section of UniProtKB is the gold standard of protein databases therefore we have used Swiss-Prot as the benchmark to define the rate of domain architecture changes during evolution of orthologous proteins since this analysis is unlikely to be affected by sequence errors. An obvious limitation of the comparison of SwissProt entries, however, is that in the case of most species only a fraction of their proteomes is represented in this database. To permit statistically significant analyses, we have selected only Metazoa species with more than 1000 entries in Swiss-Prot: Pongo-Homo) as well as protostome species (worm and fly) that diverged from deuterostomes ∼1000 Mya. Comparison of proteins from different species permitted the analysis of the influence of evolutionary distance on domain architecture of orthologous proteins.  or the protein architectures are truly different due to some type of sequence error, alternative splicing or evolutionary change in domain architecture at the gene level. These analyses have shown that a small proportion (0.1% of orthologous pairs) is false positive, consistent with the fact that the specificity of the protocol used to determine DA differences is 0.985 .Orthologous Swiss-Prot proteins with truly different DAs were subjected to additional analyses to assign them to one of the remaining categories. As described in the Experimental Section, orthologous protein sequences that differed in domain architecture in species A and B were used as queries to search the appropriate sections of various sequence databases  to decide whether the other species has a sequence that has the same domain architecture as the query. If such sequences were found it was concluded that the domain architecture difference observed in the case of Swiss-Prot entries is due either to a sequence error or to alternative splicing.i.e., the Swiss-Prot database presents different isoforms of the orthologs of different species, although their isoform pattern is similar. A typical example is agrin, where different splice forms (with different DAs) are given for different vertebrate species . SYWM_CAEEL differs from SYWM_HUMAN  in as much as it contains, in addition to the common tRNA-synt_1b domain, an extra N-terminal Pex2_Pex12 domain. This extra N-terminal region is most closely related to peroxisome biogenesis factor 10 of various species, raising the possibility that in etc.) then it was concluded that an evolutionary change has occurred at the gene level that changed the domain architecture of the encoded protein(s).If the previous steps failed to identify sequences or isoforms that eliminated DA deviation we asked whether this is due to a change in gene structure. To achieve this, the appropriate genomic regions of the orthologous proteins were subjected to gene prediction to decide whether the domain(s) distinguishing the orthologs are encoded in both genomes or not. If this analysis confirmed that the altered domain architecture is due to a change in gene structure . MUSK_HUMAN, MUSK_MOUSE, MUSK-RAT all contain three N-terminal I-set domains, an Fz domain and a C-terminal Pkinase_Tyr domain, whereas MUSK_CHICK contains an additional Kringle domain between the Fz and Pkinase_Tyr domains . Since aComparison of orthologs of DCBD1  provides an example for a more recent change in DA (TreeFam tree TF330156). DCBD1_MOUSE differs in domain architecture from DCBD1_HUMAN in that it lacks the C-terminal discoidin (F5_F8_type_C) domain . AlthougAnother example of DA change reflecting gene rearrangement is seen in the evolution of neurotrypsin (TreeFam tree TF329295). The domain architecture of vertebrate orthologs of NETR_HUMAN  were found to contain a Kunitz_BPTI domain, it seems likely that this internal domain was gained in the vertebrate lineage.Most examples of domain gain come from comparison of more distantly related orthologs, primarily comparisons across the vertebrate/invertebrate boundary. For example, the vertebrate orthologs of the amyloid precursor A4_HUMAN (TreeFam tree TF317274) have the same domain architecture but differ from those of A4_CAEEL and A4_DROME in containing an internally inserted Kunitz_BPTI domain . Since nA major category of DA alterations includes expansion and shrinkage of tandem arrays of internally duplicated domains. For example, the DAs of DMBT1_HUMAN and DMBT1_MOUSE (TreeFam tree TF329295) differ only in the number of tandem SRCR and CUB domains.Mus musculus-Homo sapiens comparisons 74% of the 167 cases belong to the category where DAs differ in a single domain , pairs that differed in two domains , three domains … N domains were increasingly less frequent. For example, in the case of e domain .etc.) has failed to identify any true case of domain replacement indicating that domain-replacement is much rarer than gain/loss of domains. The cases identified as belonging to this category proved to be false positives, primarily as a consequence of ambiguity in assignment of Pfam A domains. As a typical example we mention the case of vertebrate agrins  of the same domain clan . Such a difference might be automatically assigned to the domain-replacement category whereas the truth is that no DA change distinguishes the DAs of agrins of human and chick.Analysis of the relative frequency of orthologous pairs that do not differ in the number of domains but differ in the number of types of domains  with respect to the position of the distinguishing Pfam A domain(s) it was noted that internal Pfam A domain differences were less frequent  than those at N-terminal  or C-terminal positions . The highest proportion of DA differences  originated from gain/loss of tandem duplicated domains. In 10% of comparisons the DA differences was not assigned to any of the above categories (one of the orthologs did not contain a Pfam A domain), whereas 21% of the comparisons yielded identical DA at one of the cut-off values.When we classified DA differences of human Swiss-Prot proteins and their Swiss-Prot orthologs from To examine whether the greater frequency of terminal DA alterations reflects a greater probability of fusion-type events than insertion-type events or is due to the preponderance of the one-domain ↔ two-domain transition type  we have analyzed the positional distribution for type 1 transitions (one-domain ↔ two-domain transitions), type 2 transitions (two-domain ↔ three-domain transitions) and for type 3 transitions  separately .N-terminal, C-terminal and internal positions for DA change) the proportion of DA changes at the three different positions is quite similar: on average 25.9%, 24.3% and 29.4% of the DA changes were of the N-terminal, C-terminal and internal type, respectively  there was a significant shift in favor of internal DA changes: on average 10.2%, 6.2% and 40.3% of the DA changes were of the N-terminal, C-terminal and internal type, respectively. For example, in the case of chick-human orthologs the values for N-terminal-, C-terminal- and internal DA differences were 3%, 6% and 53% and the corresponding values of mouse-human orthologs were 8%, 6% and 45%, respectively. It may be noted that the proportion of DAs that differ only in the number of tandem copies of a Pfam A domain type is higher in the case of type 3 transitions (on average 33.3%) than in the case of type 2 transitions (on average 20.0%). This difference is explained by the fact that the category of type 3 transitions is enriched in multidomain proteins with a large number of domains, many of which contain tandem arrays of the same the domain type .Pongo abelii, Mus musculus, Gallus gallus, Xenopus tropicalis, Danio rerio, Drosophila melanogaster or Caenorhabditis elegans  one should be cautious in drawing conclusions as to the relative frequency of the different types of gene rearrangements. Nevertheless, we wish to point out that we noted no preference of terminal over internal DA changes.In view of the relatively low number of cases where the DA of a human Swiss-Prot entry was found to differ from of its Swiss-Prot ortholog from 2.3.Homo-Homo: 4.8% vs. 0.00%; Homo-Mus: 4.1% vs. 1.1%; Homo-Gallus: 4.4% vs. 3.0%; Homo-Xenopus: 4.7% vs. 0.9%; Homo-Danio: 5.0% vs. 2.1%; Homo-Drosophila: 9.4% vs. 4.8%; Homo-Caenorhabditis: 12.3 vs. 5.9% .Comparison of the DA of TrEMBL sequences of worm, fly and several vertebrate species with those of orthologous/equivalent human Swiss-Prot sequences revealed that the rate of DA deviation was always higher than in the case of Swiss-Prot/Swiss-Prot comparisons compare . For exaThe explanation for this difference between Swiss-Prot/Swiss-Prot and TrEMBL/Swiss-Prot comparisons is that the TrEMBL database is significantly contaminated with incomplete  or chimeric protein sequences  and thisMus musculus-Homo sapiens comparisons 57.94% of the cases belong to the category where DAs differ in a single domain, whereas this value is 74% in the case of Swiss-Prot/Swiss-Prot comparison. This shift is also explained by the fact that incomplete and chimeric sequences significantly contaminate TrEMBL and these are more likely to differ in multiple domains.Analysis of the relative frequency of orthologous pairs of Swiss-Prot/TrEMBL sequences that differ in the number of domains by 1, 2, 3… N domains revealed that—similarly to the observations on Swiss-Prot/Swiss-Prot comparisons—the pairs differed most frequently in single domains , pairs that differed in two domains, three domains … N domains were increasingly less frequent . HoweverIn harmony with this interpretation, comparison of the positional distribution of DA differences in TrEMBL/Swiss-Prot comparisons with those observed in Swiss-Prot/Swiss-Prot comparisons revealed that there is a significant shift in favor of terminal over internal differences. Whereas in the case of type 2 transitions of Swiss-Prot/Swiss-Prot comparisons the proportion of N-terminal (26%), C-terminal (24%) and internal (29%) DA changes were comparable see  in the cAn inspection of the data shown in vs. C-terminal DA change shows a strong preference for DA difference at the N-terminal end, suggesting that larger cDNAs (encoding larger multidomain proteins) are more likely to be incomplete at their 5′ end.In the case of multidomain proteins with a larger number of constituent domains .Comparison of the DA of human, mouse, chicken, frog, zebrafish, worm and fly protein sequences of the RefSeq database with the corresponding/orthologous human Swiss-Prot entries revealed that, similarly to the case of Swiss-Prot/Swiss-Prot comparisons the percentage of orthologs with different DA increased with the evolutionary distance of the species compared . It shouMus musculus-Homo sapiens comparisons 74% of the cases belong to the category where DAs differ in single domains in both the Swiss-Prot/Swiss-Prot comparisons and Swiss-Prot/RefSeq comparisons , pairs that differed in two domains (17% of the cases), three domains (6% of the cases)… N domains were increasingly less frequent. Note that these values are similar to those observed in the case of Swiss-Prot/Swiss-Prot comparisons. For example in the case of  compare .When we analyzed the positional distribution of DA differences and compared them with those observed in the case of Swiss-Prot/Swiss-Prot comparisons, we noted differences only in the case of type 3 transitions . Here th2.4.2.versus Swiss-Prot/Swiss-Prot comparisons may be due to mispredicted sequences contaminating the RefSeq database.As discussed in section 2.4.1, a major difference between Swiss-Prot and RefSeq databases is that the latter contains a relatively high proportion of hypothetical predicted sequences whereas the majority of Swiss-Prot entries are experimentally verified sequences. It seemed therefore plausible to assume that the higher rate of DA difference observed in Swiss-Prot/RefSeq comparisons As a further test of the validity of this explanation we have analyzed a dataset that contained only predicted sequences: the dataset of NCBI's GNOMON-predicted sequences. In harmony with our expectation the rate of DA deviation was found to be higher in comparison of human Swiss-Protein entries with orthologous GNOMON predicted sequences than in comparison of human Swiss-Protein entries with orthologous Swiss-Prot sequences or in comparison of human Swiss-Protein entries with orthologous RefSeq sequences compare .versus NCBI's GNOMON-predicted sequences is most obvious when we compare the rate of DA deviation of human entries identified as equivalents of human Swiss-Prot entries: in the case of Refseq database 0.4% of the human Refseq entries differ in DA from a corresponding Swiss-Prot entry whereas it is 7.90% in the case of NCBI's GNOMON-predicted sequences. This tendency is also obvious in the case of Danio rerio sequences where the proportion of DA differences is markedly different for Swiss-Prot entries (2.1%), Refseq entries (4.7%) or for NCBI's GNOMON-predicted entries (9.9%).This difference between Swiss-Prot, Refseq Mus musculus-Homo sapiens comparisons only 56% of the cases belong to the category where DAs differ in single domains in Swiss-Prot/GNOMON comparisons whereas in the case of Swiss-Prot-Swiss-Prot and Swiss-Prot/RefSeq comparisons this value is 74% , pairs that differed in two domains (18% of the cases), three domains (7% of the cases)… N domains were increasingly less frequent . When we compare . This obversus 10.2%; C-terminal change 15.08% versus 6.2%). This shift in favor of terminal DA changes probably reflects the presence of mispredicted sequences that are most likely to differ in DA from their complete orthologs at the N-terminal or C-terminal ends.Analysis of the positional distribution of DA differences observed in the case of Swiss-Prot/GNOMON comparisons  revealedvs. 33%, 30% vs. 20%, and 22% vs. 15%, respectively. The most plausible explanation for the dominance of DA change at the N-terminal end is that it reflects the fact that N-terminal exons are predicted with lower accuracy than C-terminal exons [Similarly to Swiss-Prot/Swiss-Prot and Swiss-Prot/RefSeq comparisons, in the case of Swiss-Prot/GNOMON comparisons N-Terminal DA deviation always exceeds that observed at the C-terminal end. As shown in al exons .2.4.3.Homo-Homo: 1.09% vs. 0.00%; Homo-Mus: 1.57% vs. 1.1%; Homo-Gallus: 3.9% vs. 3.0%; Homo-Xenopus: 5.76% vs. 0.9%; Homo-Danio: 3.27% vs. 2.1%; Homo-Drosophila: 9.01% vs. 4.8%; Homo-Caenorhabditis: 10.78 vs. 5.9%).The data obtained by comparison of the DA of human, mouse, chicken, frog, zebrafish, worm and fly protein sequences of the EnsEMBL database with the corresponding/orthologous human Swiss-Prot entries were similar to those obtained in RefSeq/Swiss-Prot and GNOMON/Swiss-Prot comparisons in as much as the percentage of orthologs with different DA increased with the evolutionary distance of the species compared and that the proportion of domain architecture differences was always higher than in the case of Swiss-Prot/Swiss-Prot comparisons . For exaMus musculus-Homo sapiens comparisons 66% of the cases belong to the category where DAs differ in single domains in Swiss-Prot/EnsEMBL comparisons, whereas in the case of Swiss-Prot-Swiss-Prot and Swiss-Prot/RefSeq comparisons, this value is 74% , pairs that differed in two domains (18% of the cases), three domains (7% of the cases)… N domains were increasingly less frequent . Compari 74% see .vs. 25.9%, internal changes: 9.2% vs. 29.4% . The same tendency was also observed in the case of type 3 transitions: in the case of EnsEMBL/Swiss-Prot comparisons the proportion of internal change was lower, those of terminal changes were higher than in the case Swiss-Prot/Swiss-Prot comparisons: N-terminal changes: 26.3% vs. 10.2%; C-terminal changes: 15.7%% vs. 6.2%; internal changes: 14.8%% vs. 40.3% . Significantly, in the case of Swiss-Prot/EnsEMBL comparisons the proportions of N-terminal and C-terminal DA deviations exceed that observed at internal positions even in the case of type 3 transitions. This is in sharp contrast with the other databases, except the TrEMBL database . In other words, the DA differences due to contamination of databases with mispredicted sequences exceed those that result from genomic rearrangements.It must be pointed out that in the case of comparison of human Refseq sequences with orthologous GNOMON predicted sequences the DA difference significantly exceeds those observed in the case of Swiss-Prot/Swiss-Prot comparisons. For example, The increase in DA difference due to sequence errors is most spectacular in the case of human/vertebrate comparisons but less striking in the case of human/invertebrate comparisons. This difference is probably explained by the fact that the problems of gene prediction are less severe in the case of invertebrates (such as worms and fly) that have less intron-rich genomes than vertebrates.These studies indicate that when predicted proteomes are compared the rate of DA difference resulting from misprediction may be higher than that arising from gene rearrangements. As a corollary, the influence of evolutionary distance on DA difference observed in the case of Swiss-Swiss-Prot, Swiss-Prot/RefSeq comparisons is barely detectable in the case of RefSeq/GNOMON comparisons compare .2.6.To test our explanation that mispredicted sequences account for the increased rate of DA deviation observed in the case of Swiss-Prot/RefSeq, Swiss-Prot/EnsEMBL, Swiss-Prot/NCBI, RefSeq/RefSeq and RefSeq/GNOMON comparisons we focused on proteins where orthologous pairs differed in DA only in the case of these comparisons but not in the case of Swiss-Prot/Swiss-Prot comparisons .EnsEMBL, RefSeq and NCBI/GNOMON sequences thus selected were subjected to in-depth analysis . These analyses have confirmed that in the case of comparisons of human Swiss-Prot entries with orthologous EnsEMBL, RefSeq or GNOMON-predicted sequences the DA differences are primarily due to sequence errors . In the case of some genes and genomes  misprediction was ‘forced’ in the sense that it was a consequence of the relatively poor quality of the genome sequence (presence of unsequenced regions) rather than the poor performance of gene prediction methods.As an example we may quote the case of XP_426568, the chicken ortholog of FZD8_HUMAN. FZD8_HUMAN and its orthologs all contain an Fz and a Frizzled domain . In contrast with this, the chicken ortholog predicted by GNOMON (XP_426568) lacks the N-terminal Fz domain . The facXenopus tropicalis (NP_001116927) lacks an N-terminal kringle domain. The fact that the protein also lacks a signal peptide cautions that it is N-treminally truncated . KREM1_HUMAN, KREM1_MOUSE, KREM1_RAT, KREM1_XENLA and KREM2_HUMAN, KREM2_MOUSE have identical DA: they all contain a Kringle, a WSC and a CUB domain . In contrast with this, the Refseq ortholog of kremen 1 from protocol . Note thXenopus tropicalis (NP_001072931) differs from those of its orthologs and paralogs in that it is C-terminally truncated therefore it lacks the C-terminal CUB domain (as well as the transmembrane region). The C-terminal part of the WSC domain is also missing from this hypothetical protein  is C-terminally truncated when compared with its mammalian and fish orthologs: whereas the DA of the latter contain TSP_1/Ldl_recept_a/MACPF/TSP_1/Sushi/Sushi domains, the chicken protein lacks the three domains downstream of the MACPF domain. Analysis of the genome of Gallus gallus and EST databases permitted the correction of this incomplete sequence with the help of the FixPred protocol and the prediction was verified by cloning the full-length cDNA of the protein  is N-terminally truncated when compared with its mammalian orthologs: whereas the DA of the latter contain an Inhibitor_I29 and a Peptidase_C1 domain, the chicken protein lacks the Inhibitor_I29 domain. The fact that unlike the mammalian orthologs the chicken protein XP_001232765 lacks a signal peptide is another indication that the sequence is mispredicted . Correction was made possible by the use of EST sequences and the prediction was verified by cloning the full-length cDNA of the protein : the pairs thus selected had identical domain architecture as evidenced by Swiss-Prot annotation. This ‘dataset of pairs with identical DA’ was used to assess the false positive rate (α) and specificity (1−α) of detection of domain architecture differences from the equation α = FP/(FP + TN). In these calculations FP  equals the number of entries that were identified with a given method as differing in domain architecture , whereas TN (True Negative) equals the number of entries that were not identified by the method as differing in domain architecture (and they do not differ in architecture).Second, to mimic changes in domain architecture of homologous proteins we have generated a dataset of 500 sequences by deleting (or inserting) randomly selected Pfam A domains from or into various  positions of one member of the pair of orthologous sequences. This dataset was used to assess the false negative rate (β) and sensitivity (1-β), calculated from the equation β = FN/(TP + FN), based on comparison of the proteins of altered DA with their original orthologs. In these calculations FN  equals the number of entries that were not identified with the given method as differing in domain architecture from their orthologs , whereas TP (True Positive) equals the number of altered entries that were correctly identified by the method as differing in architecture from their original orthologs.Sequences with artificially altered DA were also used to test the influence of domain architecture changes on the reliability of the best-hit approach used in this study to establish orthology. In these tests the “parent sequences” were replaced by the “derived sequences” in the original collection of Swiss-Prot entries and the best-hit procedure was repeated. Our results confirmed that correct orthology assignment by the triangulation method is insensitive to changes in domain architecture.3.3.2.−9, <10−8, <10−7, <10−6, <10−5 were recorded. In the case of overlapping hits the hits with the lowest E value were selected.First, the domain architectures of the collection of orthologous Swiss-Prot entries with identical domain architectures were determined by RPS-BLAST against the Conserved Domain Database ,57. In t−5) cut-off value used. This type of error occurred primarily in the case of small domains  or domains of low conservation where E-values tend to be close to the least stringent cut-off value. For example, although AGRIN_CHICK and AGRIN_HUMAN align over their entire length, at e-values of <10−2, all four EGF domains are detected in the AGRIN_CHICK, whereas only three of these domains are detected in AGRIN_HUMAN  were subjected to in-depth analyses using additional tools of domain identification, including Pfam ,9 and SMUMAN see .etc. To overcome this problem we have tested protocols in which two domain architectures were considered to be identical if they had the same number and sequential order of Pfam A domains with at least one of the E-value score cut-off value ranges of <10−9, <10−8, <10−7, <10−6, <10−5, <10−4, <10−3, <10−2 even if they differed at any single cut-off value. According to this protocol, if sequence A had the same domain architecture at <10−7 as sequence B at <10−4, then it was concluded that they have the same domain architecture.To decrease the number of false positives due to this type of error, we have tested protocols in which an additional step was included: domain architectures of positives from the CDD step were determined using the programs of the HMMER 2.3.2 software package and the Pfam HMM libraries  and domavs. EGF_CA, Kazal_1 vs. Kazal_2 etc.). For example, AGRIN_HUMAN and AGRIN_CHICK appear to have different DA because Pfam assigns their equivalent Kazal domains to different families  of the Kazal clan  of the detection of DA differences had an opposite effect on false negative rate (and sensitivity): comparison of the specificity and sensitivity of the various protocols revealed that protocols with higher specificity  had lower sensitivity  and vice versa.The protocol with the highest average sensitivity and specificity value of (Sn + Sp) / 2 =  / 2 = 0.925 was selected to compare the domain architecture of proteins. This protocol consisted of the following steps:−5 were recorded, overlapping hits were eliminated and the DA  was determined. The DA of orthologous pairs was compared and in the case of DA difference their DA was recalculated using the programs of the HMMER 2.3.2 software package and the Pfam HMM libraries at four different e-value cut-offs: <10−2, <10−3, <10−4 and <10−5.The protein sequences were searched for the presence of domains using RPS-BLAST against the Conserved Domain Database using Pfam-derived position-specific scoring matrices. Domain hits with an e-value of <103.3.3.−5 were also analyzed by comparing their DA at less stringent cut-off values . On the basis of the results of these analyses DA differences were classified with respect to:As mentioned above homologous sequence pairs identified by our protocol as differing in DA at e-value <10(a)etc. differences , its dependence on evolutionary distance of the sequences compared, its dependence on the quality of sequences compared, etc. Note that this parameter will not detect DA changes that lead to no change in the number of constituent domains .The motivation for this classification is to define the frequency distribution of one-, two-, three-domain, (b)vs. domain gain/loss and its dependence on evolutionary distance of the sequences compared, its dependence on the quality of sequences compared etc.Note that in the case of simple gain/loss of domains  this parameter is the same as the first parameter, but DA changes that lead to no change in the number of constituent domains  will also be detected: ABC↔AFC is calculated to differ in domains B and F. The goal of this analysis is to asses the relative frequency of domain replacement (c)The motivation for this classification is to get an insight into the relative contribution of different genetic mechanisms to DA changes. The rationale is that different genetic mechanisms have different position-preferences: whereas gene-fusion and fission may results in the addition/deletion of domain(s) only at the N-terminal or C-terminal end, domain-shuffling  does not have this restriction.One member of the pair has extra Pfam A domain(s)–different from the adjacent Pfam A domain-type(s)–at the N-terminal end of shared domain(s). This type of difference is classified as N-Terminal Domain Difference .One member of the pair has extra Pfam A domain(s)–different from the adjacent Pfam A domain-type(s)–at the C-terminal end of shared domain(s). This type of difference is classified as C-Terminal Domain Difference .One member of the pair has extra Pfam A domain(s)-different from the adjacent Pfam A domain-types(s)–between shared Pfam A domains. This type of difference is classified as Internal Domain Difference .One member of the pair has an extra Pfam A domain–identical in type with an adjacent Pfam A domain. This type of difference is classified as Domain Duplication Difference .If one member of the pair did not contain any Pfam A domain the relative position of the Pfam A domain in the other homolog was not assigned to any of the above categories. These pairs were entered into the Positionally Not Assigned category.The two members of the pair had identical DA at a given cut-off value. These data were entered into the Identical Domain Architecture category.In this analysis we examined the position of the distinguishing Pfam A domain(s) relative to shared domain(s). Based on this analysis the pairs of homologs were assigned to the following categories:Note that since our protocol of DA comparison uses four different cut-off values, four assignments are made for each ortholog pair. In the most unambiguous cases of DA differences the given pair is assigned four times to the same category but in many cases the pair may be assigned to different categories at different cut-off values. Also note that a given pair may show more than one type of difference therefore the given pair may be assigned to more than one category, therefore the sum-total of the assignments may be greater than 4-times the number of pairs compared. For example, the pair ABCDE ↔ BCD (change at both termini) is assigned to both category 1 and category 2.It may be pointed out that the classification according to the positions of Pfam A domains that distinguish the DAs may introduce a positional bias even if we assume that the probability of DA changes are similar at all positions of the multidomain protein outside the domain boundaries (note that domain-shuffling rarely inserts domains within domain boundaries). As a corollary, in the case of DA changes of the one-domain ↔ two-domain transition-type , DA change by definition can only be classified as terminal .i.e., single-domain proteins are the most abundant, whereas proteins containing larger numbers of domains are increasingly less frequent [vs. terminal changes.Since mathematical analyses of the distribution of multidomain proteins according to the number of different constituent domains have revealed that their distribution follows a power law, frequent  this facIn contast with this, in the case of two-domain ↔ three domain transitions , the distribution is unlikely to be biased, whereas in the case of three-domain ↔ four-domain transitions  and in the case of multidomain proteins with a large number of constituent domains internal changes may become increasingly favored .In order to analyze the contribution of this factor to the positional distribution of DA changes, we have also categorized single domain DA changes whether they belong to the one-domain ↔ two domain transitions (type 1 transitions), the two-domain ↔ three domain transitions (type 2 transitions) and the N-domain ↔ N+1-domain transitions, where N is greater than 2 (type 3 transitions).3.4.etc.) on domain architecture of hypothetical proteins we have generated datasets of sequences from human Swiss-Prot entries that mimic these errors as described previously [etc. residues from their N-terminal end or their C-terminal end. Another group of datasets were obtained by deleting the second, third, fourth, etc. 50 or 100 residue-segments of the proteins to study the effect of internal deletions. Terminal extensions or internal insertions were simulated by addition/insertion of 50 or 100 amino acid segments (with random sequences and average amino acid composition) to the N-terminal and C-terminal end or after positions 50, 100, 150, etc. of these proteins.To study the influence of gene prediction errors  insertion of 100 residues taken at random from other proteins.Note that these datasets mimic only gene prediction errors that do not disrupt the reading frame. Obviously, mispredictions that result in reading frame-shift will lead to truncation downstream of the point of such misprediction and are likely to lead to C-terminal DA change.3.5.We have used the MisPred/FixPred pipeline for the correction of erroneous sequences. As outlined in a previous paper, the MisPred protocol is useful not only for the identification of sequence errors but it also guides the correction of errors .In the case of DA deviation of closely related orthologous sequences the FixPred protocol first tests whether the DA difference is valid or not. If the ‘suspicious’ sequence gives significant alignment over its entire length and in-depth analysis with Pfam rejects DA deviation it is assigned to the false positive category. Conversely, if the region containing the Pfam A domain responsible for the deviation is missing from the other ortholog it is assigned to the true positive category.In the case of true positives it is first tested whether there is evidence for the existence of sequence versions of the orthologs  that do not differ in DA. If search of various protein and nucleic acid sequence databases provides experimental evidence for the expression of such sequences it is concluded that the DA difference observed results either from a sequence error  or alternative splicing. (Note that predicted sequences are disregarded in this step.)If the previous steps fail to find experimental evidence for sequence versions that do not differ in DA from its orthologs it is tested whether there is genomic and/or EST evidence for the presence of the domain that is missing from one of the orthologs.Accordingly, the genomic region containing the gene for the suspicious sequence is subjected to gene-prediction tools and it is asked whether there are alternative predictions that eliminate the DA deviation. In this step we employ AUGUSTUS ,61, WiseIf such predictions are found, it is concluded that no DA change occurred at the genome level. Conversely, if no viable gene models are found that eliminate DA deviation, it is concluded that a genomic rearrangement may underlie the observed DA change. It must be pointed out however, that failure to eliminate DA deviation by alternative gene prediction does not necessarily mean that a DA change resulted from genomic rearrangement: sometimes the missing domain is hidden in unsequenced genomic regions. As we have emphasized in a previous paper, another major source of gene structure errors is the incorrect assembly of genomic contigs . In suchThe reliability of the FixPred protocol was checked experimentally in a few cases by cloning full-length cDNAs of genes whose mispredicted sequences were corrected by the FixPred protocol. Sequences corrected by the FixPred protocol are deposited in the FixPred database , whereas4.We have shown that in the case of DA comparisons involving Uniprot/TrEMBL, RefSeq, EnsEMBL and NCBI's GNOMON predicted orthologous protein sequences of Metazoan species the contribution of erroneous  sequences to domain architecture differences of orthologous proteins may be greater than those of true gene rearrangements. In other words, if we observe a difference in the DA of orthologous predicted sequences it is more likely to reflect a sequence error than true genomic rearrangements.A practical consequence of this observation is that the domain architecture comparison protocol described in the present work may serve as a tool for the quality control of gene predictions and may also guide the correction of sequence errors (as illustrated in An important implication of our findings is that the presence of erroneous sequences in public databases may have led to some erroneous conclusions about the DA evolution of multidomain proteins. First, confusion of DA changes due to sequence errors with those resulting from genomic rearrangements results in a significant overestimation of the rate of DA change during evolution of multidomain proteins. More importantly, we have shown that erroneous sequences are more likely to differ in DA from the correct sequence at terminal than internal positions, thus the use of sequence databases contaminated by erroneous sequences introduce a significant bias in favor terminal over internal DA changes and may lead to erroneous conclusions about the mechanisms involved in DA evolution of multidomain proteins.In view of these findings we present a reassessment of the DA evolution of multidomain proteins in an accompanying paper .