The dough development time, crust redness, hardness, and chewiness values increased while optimum water absorption of dough, specific volume, lightness, springiness, and resilience of bread decreased significantly (p < 0.05) with increasing incorporation of OFSP and pumpkin flour in the bread formula. Additionally, the specific volume, crust redness, crumb hardness, and chewiness of the composite bread increased significantly (p < 0.05) with increasing baking temperature from 150 to 180 °C but reduced at higher baking temperatures (≥ 190 °C). The staling rate declined with increased OFSP and pumpkin flour whereas increasing the baking temperature and time increased the bread staling rate. The optimized formula for the composite bread was 78.5% wheat flour, 11.5% OFSP flour, 10.0% pumpkin flour, and baking conditions of 160 °C for 20 min. The result of the study has potential applications in the bakery industry for the development of functional bread.Orange-fleshed sweet potato (OFSP) and pumpkin fruit are underutilized crops with great potential for the production of high-quality bread with health-enhancing properties. However, the incorporation of nonconventional flour in bread formula may influence the dough and bread quality properties. This study investigated the effect of partial substitution of wheat flour with OFSP (10–50%) and pumpkin flour (10–40%), baking temperature (150–200 °C) and baking time (15–25 min) on the quality properties of the composite dough and bread using response surface methodology (RSM). Dough rheological, bread physical and textural properties were analyzed, modelled and optimized using RSM. Satisfactory regression models were developed for the dough and bread quality attributes (R Cucurbita spp) is rich in ß-carotene, dietary fibre, vitamin C, polyphenolics and flavonoid compounds [Adequate consumption of vital food nutrients and bioactive compounds such as vitamins, dietary fibre, carotenoids, polyphenols, flavonoids, and polysaccharides is crucial for good health –3. The sompounds . Despiteompounds , 11 whilBread is a highly consumed baked food among different groups globally as a major source of energy , 16. HowSeveral studies were performed on the development of new bread products using nonwheat flour ingredients like potato , sweet pIpomoea batatas L.cv.CRI-Apomuden) harvested 100 days after planting were purchased from a commercial farm located in Dambai, Krachi East Municipality of Oti region, Ghana. Mature and ripened pumpkin fruits (Cucurbita moschata) were purchased from the local market of the Tamale Metropolis in the Northern region of Ghana. Wheat flour (type 550), fresh yeast (Frischback-Hefe), margarine, salt, and sugar used for bread preparation were obtained from the Aldi-Nord supermarket in the city of Goettingen, Germany.Orange-fleshed sweet potato (OFSP) roots  design of the Design-Expert software version 11  was used to generate 27 experimental design points which consist of 16 actual model points, 5 replication and lack-of-fit points each and additional central repeated points. The low and high constraints of flour mixture were wheat flour (40–80%), OFSP flour (10–50%) and pumpkin flour (10–40%) while the baking temperature and time were between 150 and 200 °C and 15–25 min, respectively. Bread from 100% wheat flour was used as the control. The investigated response variables were dough farinograph properties, bread characteristics such as loaf volume, specific volume, moisture content, water activity, CIELAB colour coordinates , and crumb textural profile.The experimental bread samples were prepared using the straight-dough baking procedure described in ICC standard method No.131  with fewThe moisture, crude protein, crude fat, crude fibre, and ash contents of the flour was analyzed per the standard protocols of the Association of Official Analytical Chemists . Each anThe total available carbohydrate was determined by the difference method shown below:The farinograph test was performed on the wheat, OFSP, and pumpkin flour blends and the 100% wheat flour using a farinograph  following the American Association of Cereal Chemists Method 54–21 . The far3) was measured according to the rapeseed displacement procedure the standard method 10–05.01 of AACC [The mass (g) of each loaf was measured 2 h after baking using a precision balance  that has an accuracy of ( ±) 0.001 g. The loaf volume (cm of AACC . The speL*), redness/greenness (a*) and yellowness/blueness (b*) of the raw flour and bread samples were measured with a chroma meter  following the procedure described by Chikpah et al. [The CIELAB colour coordinates for lightness  after 2 h of baking and 24 h of storage respectively.Textural profile analysis (TPA) of the freshly baked bread and 24 h stored bread samples were measured with a texture analyzer  following the procedure described previously with slight modification . The texYi) to a desirability value (di); where values of di range from 0 (unfavourable) to 1 (most favourable). A global desirability value was then computed using Eq.  to 1 (most acceptable product). Therefore, to formulate an acceptable product the values of di must be greater than zero [The numerical optimization of the flour mixture, baking temperature and time for wheat-OFSP-pumpkin composite bread formulation was carried out following the modified desirability function approach  using Desing Eq. .4\documehan zero .R2), adjusted R2, prediction R2 and adequate precision values and insignificant lack-of-fit (p > 0.05) [The experimental data were analyzed using the response surface methodology of the Design-Expert software version 11.1.2.0  to determine the influence of the processing factors (flour proportion and baking conditions) on the investigated dough and bread quality attributes. The combined model fit summary and the model fitness statistics of the analysis of variance (ANOVA) were used in the selection of suitable models to describe the relationship between the processing factors and response variables. The best-fitted model was selected based on a model with the highest significant terms (p < 0.05), highest coefficient of determination ( > 0.05) , 52. The. Similar to the findings by Chikpah et al. [The physicochemical properties of flour influence the dough properties and quality attributes of the final baked bread . The pro1h et al. , wheat fh et al. , 14. Theh et al. –13 and pThe rheological analysis of dough is crucial because it reveals the dough mixing or handling properties of dough and is also useful for predicting the baking performance . The flo2 and adjusted R2 are greater than 0.98 and the lack-of-fit was insignificant (p > 0.05) as indicated Table The farinograph dough parameters were adequately described by the quadratic model. The model fitness statistics indicated the model and the terms  were significant (P < 0.0001), the values of Rt p > 0.0 as indic3) and specific volume (2.86 ± 0.02 cm3/g) as compared with the loaf volume of 166.50–349.94 cm3 and specific volume of 1.34–2.62 cm3/g for the composite bread products. Similar to the findings made in wheat-potato steamed bread [Table ed bread , wheat-Oed bread , and pumed bread , the loaed bread –27. Furted bread , 47.Tablw) are vital quality attributes of bread due to the effect on the microbial activity, textural and sensory properties of bread [w (0.853–0.908) than the control bread (0.916 ± 0.003) as presented in Table w was observed in the wheat, OFSP and pumpkin flour composite bread as the proportions of the nonwheat flour increased. This can be attributed to fibre and sugar in OFSP and pumpkin flour since these chemical components can bind to water and reduce the availability of free water in the food material [Moisture content and water activity , a*(redness) and b*(yellowness) values differed widely (p < 0.001) among the various formulations Table . The cruThe L*, a* and b* colour values of the bread crumbs differed significantly (p < 0.001) among the different formulations Table . IncreasThe experimental data for the L* and b* of the composite bread crust were adequately explained by a linear × linear model whereas the crust a* was sufficiently predicted by a linear × quadratic crossed model. The model fitness statistics are presented in Table Textural characteristics are vital quality attributes used to judge the freshness of bread and influence consumer acceptability . The tex1, β2, β3…………..β18 represent the regression coefficients of the model terms. Table 2 and adjusted R2 (0.9812–0.9987), prediction R2 (0.8813–9695), adequate precision (29.20–94.61) and lower coefficient of variance (0.23–2.07). The higher R2 and adequate precision (> 4) suggest the developed models have a higher degree of accuracy [The baking temperature and time have a significant (p < 0.05) influence on the textural properties of the composite bread Fig. a and d. accuracy . TherefoChanges in bread such as crumb firming during storage could influence consumer acceptability of the product . The cruw on the positive axis and these attributes of the bread were prominent in samples F10, F11 and F26. The wide distribution of the samples and their quality attributes in the four quadrants of the PCA biplot revealed the variations in the formulated bread products in terms of the investigated quality attributes.A biplot of the principal component analysis (PCA) of the wheat, OFSP and pumpkin composite dough and bread quality properties is shown in Fig. w (r = 0.776). Furthermore, OWA, loaf volume, specific volume, crumb cohesiveness, springiness and resilience have strong positive relationships  whereas crumb hardness and chewiness also exhibited a very strong positive correlation . Nonetheless, OWA, specific volume, and springiness showed a strong negative correlation with crumb hardness . This study has found that some properties of dough namely OWA could be applied to predict the physical and textural properties of the composite bread.Additionally, the relationship between the dough and bread quality properties was established by the PCA. The OWA had a significant (p < 0.01) positive correlation with crumb moisture content (r = 0.689) and aw, cohesiveness, springiness, chewiness and resilience but recorded lower values for a*, b* and hardness as compared with the optimized wheat-OFSP-pumpkin composite bread sample , and crumb moisture. A crust L* of 50 was targeted based on the acceptable top crust L* value for white-sandwich bread  whereas le Table .Table 7Ow, L*, cohesiveness, springiness, resilience and staling rate whereas dough development time, crumb hardness and chewiness values were augmented. Furthermore, the loaf volume and specific volume of the composite bread deteriorated greatly while crumb moisture increased as the baking temperature exceeded 180 °C. The crust a* value intensified with increasing baking temperature but declined greatly as the baking temperature exceeded 180 °C. The incorporation of OFSP and pumpkin flour in the bread formulation reduce staling rate in the composite bread during storage. Inversely, higher baking temperatures (≥ 190 °C) and longer baking time (≥ 21 min) increased crumb staling in stored bread. The optimized composite bread formulation was 78.5% wheat flour, 11.5% OFSP flour, 10.0% pumpkin flour, and baking conditions of 160 °C for 20 min. The results obtained in this study could be useful in the bakery industry for the production of functional bread products. Nonetheless, some limitations relating to the industrial application of OFSP and pumpkin flour include the commercial availability of OFSP and pumpkin flour, the stability of bioactive compounds such as carotenoids during flour production and storage, as well as the degradation of carotenoids during bread baking and storage processes. Moreover, the industrial application of OFSP and pumpkin flour for bread baking may require the modification of existing bread-making procedures of manufacturers. It is recommended that future research should focus on the assessment of the stability of bioactive compounds of the optimized wheat-OFSP-pumpkin composite bread during baking and storage.Dough and bread quality characteristics as influenced by wheat flour substitution with OFSP and pumpkin flours and baking conditions were successfully studied and optimized using response surface methodology. The addition of OFSP and pumpkin flour greatly reduced dough OWA, loaf bread specific volume, crumb a


Yet, there is no report established in this regard to the best of our knowledge. Therefore, 4 strains of Lactiplantibacillus plantarum and Pediococcus pentosaceus UP2 isolated from Malaysian foods that were previously reported to produce versatile extracellular hydrolytic enzymes were employed to biotransform gluten-free composite flour derived from rice, sorghum, and soybean. The SSF process was performed under 30–60% (v/w) moisture content for 7 days, where samples were withdrawn at 24 h intervals for various analyses such as pH, total titratable acidity (TTA), extracellular protease activity, soluble protein concentration, crude protein content, and in vitro protein digestibility. The pH of the biotransformed composite flour showed a significant reduction from the initial range of pH 5.98–6.67 to the final pH of 4.36–3.65, corresponding to the increase in the percentage of TTA in the range of 0.28–0.47% to 1.07–1.65% from days 0 to 4 and remained stable till day 7 of the SSF process. The probiotics strains exhibited high extracellular proteolytic activity (0.63–1.35 U/mg to 4.21–5.13 U/mg) from days 0 to 7. In addition, the treated composite flour soluble protein increased significantly (p ≤ 0.05) (0.58–0.60 mg/mL to 0.72–0.79 mg/mL) from days 0 to 7, crude protein content (12.00–12.18% to 13.04–14.39%) and protein digestibility (70.05–70.72% to 78.46–79.95%) from days 0 to 4 of SSF. The results of biotransformation of 50% (v/w) moisture content were mostly comparable to 60% (v/w) moisture content, implying 50% (v/w) moisture content was the most suitable moisture content for the effective biotransformation of gluten-free composite flour mediated by probiotics via SSF since flour quality is better at lower moisture content. As for the overall performance, L. plantarum RS5 was ranked the best strain, attributed to the general improvement in the physicochemical properties of composite flour.Staple foods produced from composite flour are considered feasible to alleviate protein-energy malnutrition (PEM). However, one of the major limitations of composite flour is poor protein digestibility. The biotransformation process mediated by probiotics  Protein-energy malnutrition (PEM) remains a major health burden in developing countries. One of the key contributing factors to PEM is the high consumption of low-quality diets , 2. FoodProtein quality relates to both protein digestibility and the bioavailability of amino acids. Although research efforts on composite flour over the years have focused on improving the protein content and lysine limitation in cereal-based food products , 8, 13, via the SSF technique on composite flour. Strains of probiotics LAB, such as Lactiplantibacillus plantarum RG14, Lactiplantibacillus plantarum RI11, Lactiplantibacillus plantarum RS5, Lactiplantibacillus plantarum UL4, and Pediococcus pentosaceus UP2, isolated from Malaysian foods were recently reported for their versatile capabilities of producing extracellular hydrolytic enzymes under SSF conditions and exhibited high amino-acids production capabilities , and protein digestibility of gluten-free composite flour derived from rice, sorghum, and soybean via SSF process conducted under different moisture contents.Previously, the effects of various modes of fermentation involving single food grains  and co-fermentation of sorghum and soybean to develop composite flour food products have been documented , 21–27. bilities , 28, 29.bilities , 30–33. L. plantarum RG14 (GU138145), L. plantarum RI11 (GU138147), L. plantarum UL4 (GU138143), L. plantarum RS5 (GU138148), and P. pentosaceus UP2 were obtained from the Industrial Biotechnology Laboratory, Department of Bioprocess Technology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia (UPM). The stock cultures were maintained in de Man, Rogosa, and Sharpe (MRS) medium supplemented with 20% (v/v) glycerol and stored at −20°C upon collection. All reagents used in this experiment were of analytical grade and purchased from Sigma-Aldrich Corporation.Rice , sorghum (Lohas organic sorghum white variety), and soybean (China OEM dried yellow seeds) grains were purchased from a local supermarket in Seri Kembangan, Selangor, Malaysia. The probiotics LAB Rice, sorghum, and soybean grains were milled separately to 0.5 mm flour and dried overnight to remove excess moisture , 34.Rice, sorghum, and soybean flour were weighed in the ratio of 45:45:10 –11, 35 aThe reviving of the stock culture was carried out following the procedure of Foo et al. . In brieg for 15 min  and washed with sterile 0.85% (w/v) NaCl solution . The cell population of RTU suspension was adjusted to approximately 109 CFU/ml using sterile 0.85% (w/v) NaCl solution [Absorbance (Abs) of 1.00 at 600 nm wavelength   ].9 CFU/ml of LAB culture and mixed thoroughly. While mixing, deionized water was added to the blend and adjusted to obtain 30% [0.3:1 (v/w)], 40% (0.4:1 (v/w)], 50% (0.5:1 (v/w)], and 60% (0.6:1 (v/w)] total moisture content (MC) of blend:water, respectively. There were two control samples in this study, whereby the positive controls were prepared by substituting the volume of LAB culture with deionized water water-treated control (WTC), whereas the negative control was prepared with the raw composite flour with no addition of deionized water and culture, which served as the non-treated control (NTC). After mixing, 150 g of the composite flour mixture was sealed in an airtight plastic bag and allowed to ferment at room temperature for 7 days. The samples were withdrawn at 24-h intervals and kept at −20°C for various analyses.Fermentation of composite flour was carried out according to the method of Alshelmani et al. . The amoEnterobacteriaceae growth, respectively. The bacterial growth was expressed as log10 colony forming unit (CFU)/g  NaCl solution, and 1 ml of the suspension mixture was serially diluted with a 10-fold dilution factor. The diluted sample was evaluated for viable cell count using MRS and Eosin Methylene Blue (EMB) agar  to determine the LAB and  (CFU)/g . All anaThe 10 g of biotransformed composite flour was mixed with 90 ml of deionized water in an Erlenmeyer flask prior to pH determination using a pH meter . Subsequent determination of TTA was carried out by titrating the solution against 0.1 M of NaOH using phenolphthalein as an indicator. The percentage of TTA was calculated with the volume of NaOH used  for the N = normality of NaOH, V = volume of NaOH, and W = weight of composite flour.where g at 4°C for 20 min . The clear supernatant was collected and filtered through a cellulose acetate membrane  and stored at −20°C  for 30 min at 30°C. The resulting mixture was centrifuged at 12,000 × at −20°C , 36 for g, 4°C for 10 min to remove the precipitates. Thereafter, 600 μl of the supernatant was mixed with 600 μl of 0.1 M of NaOH  and allowed to equilibrate at room temperature for 15 min. The Abs was measured at 450 nm. One unit of protease activity (U/mg) was expressed as the enzyme capable of hydrolyzing azocasein to produce 0.001 in Abs change per min of reaction time per mg of enzyme protein under assay conditions.Protease activity was determined using the method described by Thung . A solutThe biotransformed composite flour extract was used to determine the soluble protein concentration. In brief, 0.5 ml of the diluted extract was mixed with 0.5 ml of Bradford reagents  and incubated at 4°C for 5 min. The Abs was measured at 595 nm, and bovine serum albumin (BSA)  was used as the reference . Soluble2SO4  and 1 tablet of catalyst in a Kjeldahl flask by heating to obtain a clear straw color solution. The digested sample was subjected to a distillation and titration process using an automatic Kjeldahl nitrogen protein analyzer, and the protein content was expressed as the percentage of the protein content.The protein content of the biotransformed composite flour was evaluated, as described in the standard method 920.87 of AOAC . In briein vitro protein digestibility. In brief, 0.25 g of dried raw or biotransformed composite flour and 250 μl of deionized water (blank) were first treated with gastric digestion using 0.1 M of HCl containing 1.5 mg/ml of pepsin  and incubated at 37°C for 3 h. The mixture was then neutralized with 0.5 M of NaOH prior to pancreatic digestion using 10 ml of 0.2-M phosphate buffer solution containing 10 mg of pancreatin  and 1 ml of 0.005 M sodium azide  to prevent microbial growth. Then, the sample mixture was incubated at 37°C for 24 h. Next, 1 ml of 10% (w/v) TCA was added to the mixture and centrifuged at 500 × g for 20 min. The supernatant was collected, and the total protein content was determined using the Bradford assay method . The linear regression for the interaction between the moisture content and the biotransformation period (days) was performed for all the analyzed variables using GraphPad Prism Software .The mean of each experiment conducted in triplicates was subjected to analysis of variance (ANOVA), and the Tukey’s 10 CFU/g across 30–60% (v/w) MC on day 0, while the initial LAB population recorded in WTC was between 3.55 and 4.67 log10 CFU/g. Subsequently, on day 1 of the SSF process, an increase in the viable cell count of the selected probiotics strains was observed across 30–60% (v/w) MC, while the LAB population in the WTC remained the same. In this period, the highest viable cell count for the selected probiotic strains was recorded at 60% (v/w) MC by the RS5 strain (10.12 log10 CFU/g) followed by RG14 (10.06 log10 CFU/g), UL4 (9.55 log10 CFU/g), RI11 (9.47 log10 CFU/g), and UP2 (9.35 log10 CFU/g), whereas the lowest increase in viable cell count was noted at 30% (v/w) MC . Furthermore, the population of the selected probiotics maintained stability across 30–60% (v/w) MC from days 1 to 5, respectively, except for RG14, RS5, and UP2 strains that recorded a slight drop in the population at 50–60% on day 2 and thereafter maintained a stable growth until day 4. On the other hand, the LAB population in the WTC consistently increase from days 2 to 6 across 30–60% (v/w) MC reaching the highest viable count of 7.21 log10 CFU/g at 60% (v/w) MC and the lowest viable count of 5.34 log10 CFU/g at 30% (v/w) MC on day 5, respectively. Finally, a general decrease in the population of the selected probiotic strains and LAB in the WTC was recorded from days 5 to 7.p ≤ 0.05) on the pH and %TTA of the biotransformed composite flour. The highest reduction in pH with a corresponding increase in %TTA was recorded in the probiotic-treated composite flour at 30–60% (v/w) MC, whereas the WTC recorded the least changes. Although no significant difference (p ≥ 0.05) in the value of pH and %TTA was obtained for the treated composite, RS5-treated composite flour at 60% (v/w) MC recorded the highest reduction in pH (6.49–3.65) from days 0 to 4, with a corresponding increase in %TTA of 0.36–1.65 followed by UL4 , RG14 ; UP2 , and RI11  between days 3 and 4 and remained steady till day 7, whereas WTC recorded the lowest reduction in pH from 6.66 to 5.98 and %TTA 0.29–0.61 on day 6. A similar trend in results (p ≥ 0.05) with 60% (v/w) MC was noted at 50% (v/w) MC. The least changes in the pH and %TTA of the biotransformed composite flour were observed at 30% (v/w) MC, whereby RS5 strain-treated composite flour maintained the highest pH reduction (6.44–3.75) and %TTA (0.33–1.45) from days 0 to 5, followed by UL4, RG14, UP2, and RI11-treated composite flour from days 0 to 7, respectively.The results of the effect of moisture contents on the pH and %TTA are presented in p ≤ 0.05) between days 2 and 4 across 30–60% (v/w) MC compared to the WTC that showed a slight increase toward the end of the SSF process at 50% and 60% (v/w) MC. The highest endoprotease activity of 5.13 U/mg was demonstrated by RG14-treated composite flour at 60% (v/w) MC, followed by RI11 (4.98 U/mg), RS5 (4.88 U/mg), UL4, UP2, and WTC (1.82 U/mg). In addition, the results obtained had a similar trend to the endoprotease activity recorded at 50% (v/w) MC. Furthermore, the lowest increase in specific endoprotease activity was noted at 30% (v/w) MC by RG14 (1.78 U/mg), RS5 (1.62 U/mg), RI11 (1.39 U/mg), UL4 (1.22 U/mg), UP2 (1.19 U/mg), and WTC (0.47 U/mg), respectively. The extracellular protease activity values recorded for the probiotic in the treated composite flour were similar (p ≥ 0.05) across 30–60% (v/w) MC.The specific endoprotease activity of WTC and treated composite flour extracts is presented in p ≤ 0.05) across 30–60% (v/w) MC except for the WTC and the UP2-treated composite flour, while the NTC remained stable from days 0 to 7. However, the L. plantarum probiotic strains demonstrated a rapid and consistent increase in soluble protein concentration at 50–60% (v/w) MC from days 2 to 7, except for UL4-treated composite flour. The highest percentage of increase (p ≤ 0.05) in solubilized protein concentration was observed at 60% (v/w) MC, and UP2-treated composite flour extract showed the highest increase in soluble protein content (33.89%). RS5-treated composite flour recorded 29.31%, which was similar to (p ≥ 0.05) RG14 (27.59%) and RI11 (26.34%)-treated composite flour, while WTC recorded a significantly lower percentage of 7.94% from days 0 to 7. The least general increment in soluble protein concentration was recorded at 40% (v/w) MC .p ≤ 0.05) on the crude protein content of biotransformed composite flour during the SSF process. The WTC recorded a slight increase in protein content (11.99–12.83%) at 60% (v/w) MC from days 0 to 5, whereas the probiotics exerted a significant influence on the protein content of the composite flour at each moisture content during the 7 days of fermentation whereby the highest increase in crude protein content was recorded at 60% (v/w) MC by RG14-treated composite flour (12.10–14.39%), followed by UP2 (12.19–14.28%), RS5 (12.11–14.12%), and RI11 (12.10–13.16%) from days 0–4 of the SSF process. However, the UL4-treated composite recorded a significant decrease in protein content from days 0 to 2, with a gradual increase from days 3 to 7 at 30–60% (v/w) MC. The least increase in crude protein content among the probiotics-treated composite flour was recorded at 30% (v/w) MC.in vitro protein digestibility (IVPD) of biotransformed composite flour at 30–60% (v/w) MC of SSF. The IVPD for NTC was between 70.17 and 70.41%. A rapid increase in IVPD was observed for the probiotics-treated composite flour at 50 and 60% (v/w) MC from days 0 to 4, although the highest percentage increase (p ≤ 0.05) was recorded at 60% (v/w) MC. RG14-treated composite flour recorded the highest increase of 14.26%, followed by (p ≥ 0.05) RS5-treated composite flour, 13.17%; RI11-treated composite flour, 12.30%; UP2-treated composite flour, 11.91%; UL4-treated composite flour, 10.44%; and WTC displayed the lowest percentage of 2.69%. The least percentage increase in IVPD was observed at 30% (v/w) MC, in which RG14-treated composite flour recorded 3.10% of IVPD, followed by RS5 (2.71%), RI11 (2.69%), UP2 (2.44%), and UL4 (2.14%).The overall performance of studied probiotics was evaluated at each moisture content for all the parameters analyzed using a competitive analysis tool (Microsoft Excel Spreadsheet Version 2010). A score of 1–6 was assigned to the results obtained from WTC and probiotics-treated composite flours and expressed on a full scale of 100, as shown in in vitro protein digestibility). No significant interaction (p ≥ 0.05) was noted between the MC and biotransformation duration for the viable cell count of LAB, whereas a significant interaction (p ≤ 0.05) was observed for the pH, %TTA, extracellular protease activity, soluble protein, crude protein and in vitro protein digestibility of the biotransformed composite flour.Composite flour has a wide array of applications in the food industry, and the flour composition was selected mainly to improve the nutritional and functional properties of composite food products , 35. Thevia the SSF process  MC during the optimization of soy SSF parameters and reported a similar increase in the LAB population at 60 and 80% (v/w) MC. In the present study, Enterobacteriaceae growth was not detected on EMB agar during the 7 days of fermentation for both the NTC, WTC, and probiotic-treated composite flours. Gram-negative bacteria usually require relatively higher water activity than Gram-positive bacteria  and other metabolites during the fermentation process, which causes a reduction in pH and subsequent improvement in the organoleptic properties and safety quality of the final products . The selnditions , 54. Furnditions , and the strains . SSF pro strains ; thus, tentation .The recent application of the SSF process was focused on the production of hydrolytic enzymes , 58. ProL. plantarum treated composite flour. Subsequently, the active secretion of endoprotease enzyme and biosynthesis of amino acids by the probiotic LAB would have contributed greatly to the rapid increase in soluble protein concentration of the treated composite  in UL4 strain. PEM is prevalent in Africa and Asia, accounting for nearly 45% of mortality among children under the age of five , followed by the %TTA , specific protease activity , in vitro protein digestibility  and crude protein content . The interaction between MC and biotransformation period for the soluble protein content was not significant at 30% (v/w) MC , while 40–60% (v/w) MC was significant . No significant interaction was recorded for LAB count  regardless of the MC. Finally, a similar trend of interaction between MC and the biotransformation period was observed for all the analyzed variables; however, 50% (v/w) MC was considered the best due to the significant improvement recorded in the composite flour properties and the preference for flour quality.The relationship between the MC and biotransformation period for the analyzed parameters was considered in the study. A significant interaction between 30 and 60% (v/w) MC and the biotransformation period was observed for the pH of the biotransformed composite flour  and protein quality-related features  of the treated composite flour were considered for the overall probiotic biotransformation performance analysis. The ranking results illustrated that RS5 was the best probiotic strain for the biotransformation process at 40–60% (v/w) MC, even though the highest overall improvement or increase in value of analyzed parameters was noted at 60% (v/w) MC. RS5 strain recorded the highest reduction in pH and increase in %TTA among the probiotics-treated composite flour, and these attributes could be employed as indicators of fermentation performance, which is one of the major selection criteria for fermentation starter cultures , 84, 85.Composite flour derived from rice, sorghum, and soybean could play a significant role in addressing the burden of PEM in developing countries. Therefore, the improvement of the nutritional quality of composite flour is vital. This study has demonstrated the capability of the selected probiotic LAB to grow on composite flour and share similar traits, such as the production of organic acids and secretion of extracellular protease enzymes to cause improvement in the quality of composite flour under SSF conditions. However, the potential to produce organic acids and secrete proteolytic enzymes was strain dependent. RS5-treated composite flour exhibited the highest organic acid production and pH reduction, RG14-treated composite flour showed the highest endoprotease activity and improvement in crude protein and protein digestibility, while UP2-treated composite flour recorded the highest increase in soluble protein concentration. Overall, the RS5 strain exhibited the best performance for the SSF fermentation of rice, sorghum, and soybean composite flour. In addition, the ability of the selected probiotic LAB to thrive and transform the composite flour at 50 and 60% (v/w) MC offers an excellent advantage for commercial application. Lower moisture content will promote flour quality; thus, 50% (v/w) MC was considered to be the most suitable MC for the effective biotransformation of gluten-free composite flour of rice, sorghum, and soybean. Therefore, applying the selected probiotics as a biotransformation agent warrants further investigations into their impact on other essential nutritional components, functional properties, and health-promoting features of the composite flour.The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.HF and TL: conceptualization, funding acquisition, project administration, and resources. HF, NR, and TL: data curation, supervision, and validation. KK and HF: formal analysis, investigation, and methodology. KK, NR, and HF: writing—original draft. KK, HF, and NR: writing—review and editing. All authors have read and agreed to the published version of the manuscript.


Compared with the standard BP algorithm, the prediction accuracy of the GA-BP neural network is significantly improved, and it can be used to predict the deformation of mountain glaciers.The morphological changes in mountain glaciers are effective in indicating the environmental climate change in the alpine ice sheet. Aiming at the problems of single monitoring index and low prediction accuracy of mountain glacier deformation at present, this study takes Meili Mountain glacier in western China as the research object and uses InSAR technology to construct the mountain glacier deformation time series and 3D deformation field from January 2020 to December 2021. The relationship between glacier deformation and elevation, slope, aspect, glacier albedo, surface organic carbon content, and rainfall was revealed by grey correlation analysis. The GA-BP neural network prediction model is established from the perspective of multiple factors to predict the deformation of Meili Mountain glacier. The results showed that: The deformation of Meili Mountain glacier has obvious characteristics of spatio-temporal differentiation; the cumulative maximum deformation quantity of glaciers in the study period is −212.16 mm. After three-dimensional decomposition, the maximum deformation quantity of glaciers in vertical direction, north–south direction and east–west direction is −125.63 mm, −77.03 mm, and 107.98 mm, respectively. The average annual deformation rate is between −94.62 and 75.96 mm/year. The deformation of Meili Mountain glacier has a gradient effect, the absolute value of deformation quantity is larger when the elevation is below 4500 m, and the absolute value of deformation quantity is smaller when it is above 4500 m. The R Mountain glaciers are glaciers that develop in snowy areas above the snowline in low to mid-latitude alpine regions and flow slowly downhill along mountain slopes or troughs under the effect of gravity ,2. MountConventional means of mountain glacier monitoring are usually performed through field survey measurements, including field stereo photogrammetry , Global Interferometric Synthetic Aperture Radar (InSAR) technology has been used for conventional field surveys and optical remote sensing techniques over the past few years. InSAR technology is based on active synthetic aperture radar (SAR) technology, which shows the advantages of a large range, all-day, all-weather, good stability, strong dynamics, high accuracy, as well as high resolution . It has Mountain glaciers exhibit significant response characteristics to climate and environmental changes , and comThus, to solve the limitations in the monitoring and prediction of mountain glacier deformation, this study proposed a method that combines InSAR technology and Genetic Algorithm optimized Back-Propagation (GA-BP) neural network to monitor and predict mountain glacier deformation and built a prediction model from a multi-factor perspective. The spatial distribution and temporal evolution of mountain glacier morphological changes were analyzed, and the relationships between mountain glacier morphological variables and climate and environmental factors were also developed.The study area is located at the border between the eastern part of Zayü County in the Tibet Autonomous Region and the western part of Deqin County in Yunnan Province  . The stuThe Meili Mountain glacier area is characterized by high slopes, long gorges, and deep valleys and it is controlled by the alternating southwest Indian monsoon and temperate prevailing westerly winds, with warm and humid air currents penetrating from south to north from the three parallel river valleys of the Jinsha, Nu, and Lancang rivers, thus resulting in the formation of mountain glaciers of enormous size and abundant precipitation in the area . The Sechttps://scihub.copernicus.eu/dhus/#/home (accessed on 30 April 2022). The Sentinel-1 satellite was launched on 3 April 2014 and comprised a constellation of two polar-orbiting satellites, Sentinel-1A and Sentinel-1B. Sentinel-1 satellite carried a C-band synthetic aperture radar (SAR) with a right-viewing active phased array antenna at a wavelength of 5.6 cm, operating at a central frequency of 5.405 GHz, thus providing Stripmap (SM), Interferometric Wide swath (IW), as well as a satellite with a frequency of 5.405 GHz. Interferometric Wide swath (IW), Extra Wide swath (EW), and Wave (WV) represent four imaging modes to provide all-day, all-weather radar imagery of the land and ocean [In this study, Sentinel-1 data acquired by the European Space Agency (ESA) were employed as the primary dataset to monitor trends in glacier melting, accessed online at nd ocean .A total of 50 S1 TOPS modes single-look complex (SLC) images in Interferometric Wide swath (IW) mode, polarized in VV, with an amplitude of 250 Km, were selected for this study since horizontal polarization (VV) exhibits a stronger backscatter intensity than cross-polarization (VH) in glacial regions  and the https://www.eorc.jaxa.jp/ALOS/en/aw3d30/data/index.htm (accessed on 1 May 2022). Glacier albedo data were obtained using Landsat 8 data based on the GEE platform to obtain blue band, red band, near-infrared band, shortwave infrared 1, as well as shortwave infrared 2. Soil organic carbon content data, available online from FAO: http://54.229.242.119/GSOCmap/ (accessed 10 May 2022). Rainfall data, available online from Rainfall Processing Systems: https://arthurhou.pps.eosdis.nasa.gov (accessed 15 May 2022).The main climatic and environmental factors for mountain glacier deformation involved elevation, slope, slope orientation, glacier albedo, surface organic carbon content and rainfall in the study area. Elevation, slope, and aspect were obtained using ArcGIS 10.8 software with a DEM of the study area as the input data. The DEM data were acquired using a Digital Elevation Model (DEM) with 30 m spatial resolution from JAXA ALOS WORLD 3D, 30 m spatial resolution, to calculate elevation, slope, and aspect and remove the effects of topographic phase, available online at: http://www.ncdc.ac.cn/portal/ (accessed 20 April 2022). High-resolution Google images (Google Earth) are available online at http://www.google.cn/intl/zh-CN/earth/ (accessed 20 May 2022). The data acquired from Google Earth were employed for positional annotation of the Meili Mountain glacier dataset and for overlaying the acquired InSAR deformation results for analysis. Ancillary data consisted of the China Second Glacier Inventory dataset (V1.0), available online from the National Glacial Permafrost Desert Science Data Centre at Fusion of D-InSAR and MAI has been widely used to acquire surface displacement components and create 3D deformation fields in accordance with SAR image phase information . Based oobtained a,b. The The MAI technique is capable of generating forward and backward interferograms through sunbeam decomposition in the SAR azimuthal direction under the Doppler positive and negative conditions of the radar echoes. Subsequently, it obtains the azimuthal displacement component of the orbit in accordance with the phase difference of the interferograms . Using teocoding a,b, and In accordance with the imaging geometry of the SAR satellite, it is assumed that the 3D deformation components of the glacier in the vertical, north–south, and east–west directions are In Equation (4), a total of four observations in the line-of-sight and azimuthal directions are obtained using D-InSAR and MAI techniques, and three unknown components of 3D deformation in the vertical, north–south, and east–west directions are obtained, which are easily derived from the weighted least squares principle , as exprThe acquired 3D morphological variables of mountain glaciers cannot be analyzed in time series for glacier melting trends since they only detect displacement changes between two-time phases. Accordingly, the SBAS-InSAR technique was introduced, in the SARScape5.2.1, and the 48 Sentinel-1A single-look complex images of the study area from 9 January 2020 to 29 December 2021 were selected, with the image dated 9 March 2020 as the super master image. A temporal baseline threshold of 60 d was set, and the spatial baseline was set to 45% of the critical baseline threshold. On that basis, a total of 292 interferometric image pairs were generated. To suppress speckle noise, the multi-view number was set to 1:4, and the interferometric processing was performed using the Minimum Cost Flow decoupling method and the Goldstein filtering method. The combined interferometric pairs were aligned and adjusted to remove undesirable data to produce an interferogram, at which point the interferometric phase is expressed in Equations (6) and (7).P points a withoutThe least square method and singular value matrix decomposition were used for deformation inversion, followed by estimation and removal of atmospheric phases to obtain time series deformation information for the study area, as presented in Genetic algorithms (GAs) are stochastic global search and optimization methods developed to simulate the mechanisms of biological evolution in nature. GA use Darwinian evolution and Mendelian genetics to automatically acquire and accumulate knowledge regarding the search space during the search process and to efficiently parallel and adaptively control the global search to yield the optimal solution .The Back-Propagation (BP) neural network refers to a multi-layer feed-forward neural network trained by the error back-propagation algorithm, which learns and stores considerable “input–output” pattern mapping relationships . The BP Using the GA algorithm to filter the connection right parameters Read in the pre-processed training samples Taking The nodal error signals Back-propagation, correction of weights and calculation of residuals.Combining the GA algorithm and BP neural network can overcome the problems of slow learning convergence, easy to fall into local minima and uncertain network structure of the traditional BP algorithm, and further optimize the weights and thresholds, resulting in optimal configuration of network parameters, minimal prediction error of the test set and higher prediction accuracy. The GA-PB neural network refers to a BP neural network in which the initial weights and thresholds from the input layer to the implicit layer and from the implicit layer to the output layer are encoded and concatenated into a body. To be specific, the individuals are screened by the selection, crossover, and change operations in the genetic algorithm, and the good individuals are screened out and the poorly adapted individuals are eliminated in accordance with the fitness values of the fitness function. Furthermore, the screened good individuals are fed into the BP neural network as the initial weights and thresholds of the BP algorithm ,45, and Based on the GA-BP neural network algorithm, the factors for glacier morphology change, slope, slope aspect, elevation, glacier albedo, surface organic carbon density, and rainfall, which have been analyzed by grey correlation , were taGlacier deformation has been divided into negative and positive deformation. To be specific, negative deformation includes glacier ablation, downslope movement, and thickness reduction, while positive deformation includes glacier accumulation, upslope movement, as well as thickness increase . SentineThe major reason for the inconsistency in the deformation trends of the north–south deformation components is that the InSAR deformation observations are extremely insensitive to the deformation in the north–south direction due to the limitations of the existing SAR satellite’s polar orbit flight and side-view imaging mode . SeveralRelevant research has suggested ,8,15,17 As depicted in the above table, there was a high correlation between elevation, slope, slope orientation, glacier albedo, surface organic carbon density, and rainfall with the morphological change values of the mountain glaciers in the Meili Snow Mountain, thus suggesting that the above factors significantly affect morphological changes  and are highly correlated with the morphological changes in the mountain glaciers in the Meili Snow Mountain. Furthermore, there were significant differences in the driving forces of the morphological changes in the mountain glaciers by different factors.Based on the GA-BP neural network algorithm, the melting region was selected as the study area for glacier morphological change prediction in this study. Factor analysis was conducted on elevation, slope, aspect, surface organic carbon density, glacier albedo and rainfall to gain insights into whether the glacier morphology change factors selected in A standard BP neural network model was selected for comparison experiments with the prediction accuracy of the GA-BP neural network model in glacier surface deformation monitoring to verify the validity and feasibility of the model used in this study, and to explore the effect of the GA algorithm on the performance of the BP neural network. In the above experiments, the network functions, training parameters, and deformation datasets of the respective layer were controlled, and 700 randomly selected deformation data from the melting area were employed as the training samples. The remaining 56 deformation points were predicted, and the prediction results are presented in To gain insights into the difference in deformation characteristics between the ablation and accumulation areas, the cumulative deformation of the Meili Mountain glaciers was divided , with thMountain glacier deformation is characterized by complex causes and diverse types. since glacier deformation can be based on the assumption that it comprises seasonal non-linear deformation and long-term linear subsidence trends , this stThe results of the grey correlation values in the previous In this study, a hybrid prediction model is developed from a multi-factor perspective using a feed-forward neural network trained by a combination of genetic algorithm and back-propagation algorithm. Unlike previous prediction models using mainly historical deformation data, this method has better prediction results and lower prediction errors. The decision coefficients, convergence optimal number of iterations, MRSE, and mean relative error  verify tMountain glaciers are effective in indicating the regional-scale hydrological cycle and climate change. To solve the limitations in the monitoring and prediction of mountain glacier deformation, this study proposed a method to monitor and predict glacier deformation from a multi-factor perspective in combination with InSAR technology and GA-BP neural network. First, the time series of mountain glacier deformation and 3D deformation field were built by combining SBAS-InSAR, D-InSAR, and MAI techniques, and the spatial distribution characteristics and temporal evolution pattern of the glacier deformation in the Meili Mountain were analyzed. Subsequently, the relationship between mountain glacier deformation variables and climate and environmental factors was developed and employed as a training sample to optimize the BP neural network using genetic algorithm (GA). The following conclusions were obtained by predicting mountain glacier deformation:(1) The temporal evolution of glacier deformation in the Meili Mountain is more significant, with the maximum deformation of 135.19 mm in the differential interference line-of-sight direction, −48.76~131.33 mm in the azimuthal direction, the cumulative maximum deformation of −212.16 mm in the time series, and an average annual deformation rate from −94.62 to 75.96 mm/year, with the maximum deformation of −125.63 mm, −77.03 mm and 107.98 mm in the vertical, north–south, and east–west directions, respectively after 3D decomposition. The maximum deformation change in the vertical, north–south, and east–west directions reaches −125.63 mm, −77.03 mm, and 107.98 mm, respectively;(2) The spatial distribution of glacier deformation in the Meili Mountain exhibits strong divergence, with mountain glacier deformation being the slowest in the northwest and the fastest in the southeast along the mountain range, and with smaller mountain glaciers showing a stronger response to climate change;(3) Surface organic carbon density is a vital driver of morphological change in the Meridian Mountains, and it serves as an essential complement to the analysis of glacier morphology. The strong light absorption capacity of black carbon (BC) at the ice sheet surface directly affects ice sheet temperature, thus having an effect on the morphological change in the Meridian Mountains glaciers;(4) The 4500 m elevation value is the point of divergence characteristic of the change in the gradient of deformation of the Meridian glacier. The absolute value of the deformation variable is larger for elevations below 4500 m, and that is smaller above 4500 m;(5) The coefficient of determination, average relative error, and root mean square error of the optimized GA-BP neural network algorithm based on the genetic algorithm for predicting the glacier deformation in the Meili Mountain are 0.86 mm, 1.12%, and 10.38 mm respectively, thus suggesting that the GA-BP neural network used in this study can effectively overcome the problems of the standard BP algorithm relying on initial weights and thresholds and low accuracy, and effectively improve the network prediction. This study reveals that the GA-BP neural network is capable of effectively overcoming the problems of the standard BP algorithm  and effectively enhancing the network prediction performance and accuracy, thus providing an effective and reliable prediction of glacier deformation.


Protein domains are the basic units of proteins that can fold, function and evolve independently. Protein domain boundary partition plays an important role in protein structure prediction, understanding their biological functions, annotating their evolutionary mechanisms and protein design. Although there are many methods that have been developed to predict domain boundaries from protein sequence over the past two decades, there is still much room for improvement.In this article, a novel domain boundary prediction tool called Res-Dom was developed, which is based on a deep residual network, bidirectional long short-term memory (Bi-LSTM) and transfer learning. We used deep residual neural networks to extract higher-order residue-related information. In addition, we also used a pre-trained protein language model called ESM to extract sequence embedded features, which can summarize sequence context information more abundantly. To improve the global representation of these deep residual networks, a Bi-LSTM network was also designed to consider long-range interactions between residues. Res-Dom was then tested on an independent test set including 342 proteins and generated correct single-domain and multi-domain classifications with a Matthew’s correlation coefficient of 0.668, which was 17.6% higher than the second-best compared method. For domain boundaries, the normalized domain overlapping score of Res-Dom was 0.849, which was 5% higher than the second-best compared method. Furthermore, Res-Dom required significantly less time than most of the recently developed state-of-the-art domain prediction methods.http://isyslab.info/Res-Dom/.All source code, datasets and model are available at  The protein domain is the predominant concept in many areas of the biological sciences. At the sequence level, protein domains are considered to be homologous portions of sequences and are ‘evolutionary units’. In structural terms, domains have generally been observed as local, compact units of protein structure, with a hydrophobic interior and a hydrophilic exterior, and they form a globular-like state that cannot be further subdivided . Thus, pDue to the importance of the protein domains, many methods have been developed to delineate the domain boundaries. These methods can be divided into two major classes, namely, those that are structure-based and those that are sequence based. Structure-based methods use an experimental structure as input. DomainParser , DIAL P and Sworab initio protein folding methods, but these methods are more time-consuming. Template-based methods are conducted to find homologous templates of the query sequence and predict the protein domain of the target sequence based on the query template alignment. For example, SSEP-Domain  profile generated by HHblits (The training dataset was collected from CATH (v4.1)  which waThe HMM profile, secondary structure, solvent accessibility and embedding from ESM were chosen as the model input.The HMM profile was generated by HHblits which searches against uniclust30(2020.06). For a protein sequence of L amino acids, the dimension of the HMM profile was Secondary structure and solvent accessibility are commonly used features for domain boundary detection -score . For our Res-Dom model, there were some hyperparameters to optimize, including the number of convolution layers, the convolution filter size setting in every residual block, the number of hidden units for LSTM, and the fully connected layer dimensions. Considering the statistics of all sequence lengths and the convenience of subsequent processing and implementation, the sequence lengths were normalized to 700. Sequences longer than 700 were truncated and those shorter than 700 were padded with zeros. The other hyperparameters are optimized according to the performance on the validation set, as follows. The parameters for the ResNet are listed in Res-Dom was constructed in Python 3.6.5 with Keras 2.2.0, TensorFlow 1.8.0 and ESM 0.1.0  and 2.4% (0.860) higher than Res-Dom w/o Bi-LSTM. These results illustrate that the long-term residue interaction leaned by Bi-LSTM module was also important for the domain boundary prediction. When the input part of ESM was removed, the performance of Res-Dom degraded significantly. The ACC and MCC scores of Res-Dom w/o ESM and Res-Dom increased from 0.836 to 0.884, 0.623 to 0.742, respectively. For the domain boundary prediction, Res-Dom w/o ESM achieved an NDO score of 0.897 and a DBD score of 0.865, which was about 1.9% (0.904) and 1.8% (0.860) lower than Res-Dom. From this ablation experiment, it can be proved that the semantic representation based on the pre-trained model such as ESM can improve the prediction of domain boundaries. To make a fair comparison, we further compared Res-Dom with other state-of-the-art methods on the independent test set.Res-Dom was compared with three template-free state-of-art methods  and the template-based method (ThreaDom). It is worth noting that FUpred and ConDo use protein contact maps, while DNN-Dom only uses sequence and sequence profile information as input features to predict domain boundaries. The overall comparison results are shown in In terms of domain classification, Res-Dom achieved the highest MCC (0.668), which was about 17.6% higher than DNN-Dom (The rank 1 in three template-free state-of-art domain classification predictors) and 1.80% higher than ThreaDom. Res-Dom also produced the highest ACC (0.871), followed by ThreaDom (0.830), DNN-Dom (0.816), ConDo (0.81) and FUpred (0.789). Considering the individual metrics, Res-Dom achieved the highest recall (0.913) for single-domain proteins and the highest precision (0.756) for multi-domain proteins. Meanwhile, FUpred achieved the highest precision (0.937) for single-domain proteins, the highest recall (0.856) for multi-domain proteins and the lowest MCC (0.559). This implied that FUpred tends to classify targets as multi-domain proteins. The independent test set consisted of 252 single-domain proteins and 90 multi-domain proteins, while 39.8% (136/342) of proteins in the test set were predicted to be multiple-domains by FUpred.In terms of domain boundary prediction, Res-Dom achieved the highest NDO score (0.849), followed by ThreaDom (0.842), FUpred (0.807), ConDo (0.788) and DNN-Dom (0.777). This indicated that for Res-Dom at least 84.9% of the residues in the predicted domains overlapped with the correct domains, which was 5% higher than that of the best template-free method (FUpred) and was close to ThreaDom. As for DBD score, ThreaDom achieved the highest score (0.551), FUpred and Res-Dom achieved similar DBD scores, which were 0.504 and 0.501, respectively. ConDo and DNN-Dom achieved DBD score of 0.487 and 0.418, respectively. Overall, Res-Dom achieved state-of-the-art performance in terms of most criteria on the independent dataset.Furthermore, we also tested Res-Dom against the other three recently developed methods ConDo, DNN-Dom and FUpred on the CASP13 datasets. The results are shown in The superior performance of Res-Dom could be mainly attributed to the following two reasons. Firstly, our method distinguished itself from previous methods in that we employed the deep residual neural networks to model sequence relationships and long-term residue interaction leaned by Bi-LSTM module could assist protein boundary prediction. Second, the protein pre-training model can provide more complex high-order semantic features, improve domain prediction performance and reduce the dependence on evolutionary information.To illustrate the prediction of domain boundaries directly, protein chain 4c4aA of the independent testing datasets was taken as an example of domain boundary prediction and is shown in We compared the time complexity for Res-Dom and the other three template-free methods on the 342 test proteins. All methods run as standalone package with default parameters and the running time for 342 test proteins was calculated on a 20-core CPU. The results are shown in In this work, we developed a novel deep learning model, Res-Dom, based on the deep residual network and took advantage of the embedding generated by ESM. Res-Dom yielded much better performance than other state-of-the-art methods in terms of domain classification. The MCC value of the classified protein domains it achieved was 17.6% and 36.4% than the corresponding second-best method on the independent test dataset and the CASP 13 protein domain dataset, respectively. In terms of domain boundary prediction, Res-Dom also achieved the highest NDO score on both the independent and the CASP13 test datasets, although it got a slightly lower DBD score than FUpred which uses a predicted contact map. It should be noted that FUpred took much more time than Res-Dom on the same target. In sum, Res-Dom obtained the best performance of all the methods compared on the independent test datasets. It also achieved superior results on the CASP 13 protein domain datasets. Discontinuous domains are defined as domains that contain two or more fragments from different regions of the sequence. The prediction of discontinuous domains is still an under-solved problem, ThreaDomEx and FUpred can only partially solve it, mainly due to the fact that they contain some prior knowledge of protein structures, and most tools such as ConDo, DNN-Dom, etc. can only predict the domain boundaries of proteins. The current version of Res-Dom does not consider discontinuous domains, we intend to integrate it with DomEx to generate a more powerful tool which can predict not only domain boundary but also detect discontinuous domains.


Conventional fully-supervised approaches usually require a large amount of manually-labeled data. As more and more RS images becoming available, how to make full use of these unlabeled data is becoming an urgent topic. Semi-supervised learning, which uses a few labeled data to guide the self-training of numerous unlabeled data, is an intuitive strategy. However, it is hard to apply it to cross-dataset  scene classification due to the significant domain shift among different datasets. To this end, semi-supervised domain adaptation (SSDA), which can reduce the domain shift and further transfer knowledge from a fully-labeled RS scene dataset (source domain) to a limited-labeled RS scene dataset (target domain), would be a feasible solution. In this paper, we propose an SSDA method termed bidirectional sample-class alignment (BSCA) for RS cross-domain scene classification. BSCA consists of two alignment strategies, unsupervised alignment (UA) and supervised alignment (SA), both of which can contribute to decreasing domain shift. UA concentrates on reducing the distance of maximum mean discrepancy across domains, with no demand for class labels. In contrast, SA aims to achieve the distribution alignment both from source samples to the associate target class centers and from target samples to the associate source class centers, with awareness of their classes. To validate the effectiveness of the proposed method, extensive ablation, comparison, and visualization experiments are conducted on an RS-SSDA benchmark built upon four widely-used RS scene classification datasets. Experimental results indicate that in comparison with some state-of-the-art methods, our BSCA achieves the superior cross-domain classification performance with compact feature representation and low-entropy classification boundary. Our code will be available at  From the perspective of feature levels, current feature alignment of SSDA can be roughly divided into two types, sample alignment  and domaTo achieve the flexible and robust class-level feature alignment without a high computing burden, we devise a novel holistic SSDA framework termed as bidirectional sample-class alignment (BSCA) for RS cross-domain scene classification, which consists of two feature alignment modules:(1) an unsupervised alignment (UA) module is introduced to achieve the global distribution alignment between the feature extracted from labeled samples (containing source samples and labeled target samples) and the feature extracted from unlabeled target samples, with no demand for their class information. It is achieved by decreasing the distance of maximum mean discrepancy (MMD)  not only(2) an original supervised alignment (SA) module is proposed to achieve feature alignment from samples to their corresponding cross-domain class centers. Here, class centers are calculated by memory bank mechanism that can individually store the feature extracted from source samples and target samples. In SA, there are two separated memory banks, source memory bank and target memory bank, corresponding to two kinds of class centers. Taking the feature extracted from a source sample as an example, it plays two roles simultaneously at each training iteration: (a) it is used to update the source memory bank of the same class; and (b) it is aligned to the target class center of the same class, which is calculated from the target memory bank. Similarly, the feature extracted from a target sample is used to update the target memory bank, and is aligned to the corresponding source class center. Therefore, our SA can achieve the feature alignment both from source samples to target centers and from target samples to source centers, i.e., bidirectional alignment. Besides, benefiting from memory bank, abundant class-aware feature of historical samples can be stored and used for real-time sample-class feature alignment with a small amount of computing resources. In other words, the SA of BSCA can calculate the robust class centers from abundant class-aware feature beyond the limitation of mini-batch and support the class-wise feature alignment in an online manner.To objectively evaluate the effectiveness of the proposed BSCA, we build an RS-SSDA benchmark with the common classes of four widely-used RS image scene classification datasets, and then compare our method with some state-of-the-art SSL, UDA, and SSDA methods on this benchmark. Experimental results indicate that our BSCA outperforms the comparison methods and achieves the best mean classification performance of a total of 12 adaptation scenarios in the benchmark. Overall, our contributions can be summarized as follows:•We propose a bidirectional sample-class alignment (BSCA) method for RS-SSDA, consisting of two modules of supervised alignment (SA) and unsupervised alignment (UA). SA aims to achieve the bidirectional feature alignment both from source samples to target class centers and from labeled/pseudo-labeled target samples to source class centers. By contrast, UA focuses on the global alignment among the features extracted from labeled samples and unlabeled samples without a demand for their class information.•To verify the effectiveness of the proposed method, we collect an RS-SSDA benchmark dataset with 7 common classes of 4 widely-used RS image scene classification datasets, and implement extensive experiments based on it.•Compared with some state-of-the-art SSL/UDA/SSDA methods, our proposed BSCA obtains the best mean classification results on the above RS-SSDA benchmark, demonstrating the effectiveness of our method.22.1SSDA is a recently rising research field, which can be seen as a combination of SSL and UDA. To our best knowledge, there are roughly three kinds of SSDA methods: adversarial training based SSDA , entropy2.2In recent years, an increasing number of works have focused on the domain adaptation (DA) of RS image scene classification . In SongOur work mainly concentrates on RS image scene classification in the single-source single-target SSDA setting, with the intent to learn the transferable knowledge from a single-source domain to a single-target domain, in order to obtain the domain-invariant feature representation that would also be helpful for multi-source/multi-target research.2.3The memory bank module has been broadly applied because it can store extra information outside the neural network and can be used as a dictionary for reference. For example, a memory-augmented temporal bidirectional learning network, which can learn to write the most evident information into an external memory module, was proposed for human action recognition in 3This section begins by introducing some notations of the universal SSDA, then presents the shared network architecture, and finally describes the proposed BSCA in detail with the summary of its training procedure. The workflow of the BSCA based SSDA architecture is shown in 3.1K}, where K is the number of classes.In the setting of SSDA, there are three subdomains: a fully-labeled source domain 3.2Following some existing SSDA literature , a CNN mH, W, and C are the spatial height, spatial width, and feature dimension, respectively, is extracted from an image First, a feature map  layer G  as (2)f=lization . The terFollowing the common practice in the SSDA literature , all the3.3BSCA is composed of UA and SA, which aims to achieve the unsupervised and supervised feature alignment as shown in 3.3.1As shown in the “unsupervised alignment” part of , MK-MMD , is usedHere it is worth mentioning that instead of the only alignment between the source data and the target data, UA focuses on aligning the feature distribution between all the labeled domains (containing the source domain and the labeled target domain) and unlabeled target domain. The reason for it is that labeled target data can obtain robust and discriminative feature representation by supervised training, and they can work with fully-labeled source data to provide more comprehensive feature distribution at the domain level as a more robust anchor for the features extracted from unlabeled data.3.3.2As shown in In SA, there is a source memory bank and a target memory bank, denoted as blue rectangles represent the features extracted from source images, orange rectangles represent the features extracted from labeled target images, and yellow rectangles represent the features extracted from high-confidence unlabeled target images. Based on the memory bank, SA combines the advantages of both sample alignment and class alignment, where samples allow flexible optimization and class centers provide stable and robust anchors. Concretely, robust class centers can be calculated from multiple features stored in memory banks, without a demand for the computation-consuming feature extraction process; meanwhile, features extracted from samples of a mini-batch can be aligned to their associated cross-domain class centers in real time. As a result, such sample-class alignment takes up little extra computing resources yet achieves both flexible and robust feature alignment during the training phase.The pipeline of the memory bank based SA is depicted in The detailed process of SA can be divided into the following three steps:Generating feature-label pairs. First, it is necessary to generate the feature-label pair [(a) Updating memory banks with feature-label pairs. Then it is important to design a reasonable updating strategy to update the memory banks, which provide anchors for sample-class alignment and therefore determine the performance of SSDA. (b) On the other hand, the pseudo-labels of high-confidence unlabeled samples can also be used to train the network to improve the weight of the unlabeled target domain, as Making the cross-domain sample-class alignment. Finally, we make the cross-domain alignment between feature and class centers both from the source domain to the target domain and from the target domain to the source domain. Before alignment, it is necessary to calculate the source and target class centers, denoted as (c) The source and target class centers are used as anchors for the alignment of the features echnique . Within 3.3.3The overall loss of the BSCA-based SSDA model is the combination of supervised classification loss For a clear understanding of the workflow of our method, an iteration of the training procedure of BSCA is summarized below. In each iteration, three mini-batches of images, 4In this section, some experimental settings are introduced, including benchmark datasets and implementation details. Then two kinds of hyper-parameter tuning experiments are conducted to explore the boundary of the proposed BSCA. Following it, the experiments are conducted on the proposed method in comparison with some state-of-the-art SSL/UDA/SSDA methods. Next, the ablation study is performed to explore the individual effect of each component of BSCA. Finally, visualizations of the feature distributions of BSCA and other methods are provided for intuitive comparison.4.1Benchmark datasets. To comprehensively evaluate the proposed method, we collect and build an RS-SSDA benchmark with 7 shared classes from four widely-used RS image scene classification datasets: RSSCN7  tuning, are conducted in this subsection.The experimental results of entropy threshold tuning are reported in On the other hand, the experimental results of shot number tuning is provided in 4.4To fairly and comprehensively evaluate the effectiveness of our BSCA on RS cross-domain scene classification, based on 12 kinds of adaptation scenarios of the proposed RS-SSDA benchmark, we conduct comparison experiments between our method and some state-of-the-art UDA, SSL, and SSDA algorithms on three popular CNN backbones.4.4.1FixMatch  ADDA  FixMatch , one cla ADDA , which i, and mean classification accuracy, as shown in From the side of types of methods, the SSL method of FixMatch performs worst with a dramatic gap than UDA and SSDA, verifying that domain adaptation strategy is more valuable for target domain classification of RS scene images than the semi-supervised learning strategy. For the UDA method of ECB-FAM, its performance is affected by source domains. For example, for the same target domain of NWPU-RESISC45 on VGG16, its accuracy decreases from 90.5% (AID is the source domain) to 74.6% (RSSCN7 is the source domain), with a gap of 15.9%. In contrast, as the combination of SSL and UDA, most of SSDA methods in experiments have more superior and stable performance, less influenced by source domains. The possible reason is that SSDA combines the advantages of UDA and SSL and therefore becomes more robust.From the point of view of SSDA methods, our BSCA achieves the best and most stable performance of all the SSDA methods tested. Compared with the basic method of S+T, SSDAN shows the meaningful improvement over ResNet34 and VGG16. However, SSDAN only obtains a small gain in EfficientNet_B0, which reveals its instability when facing different CNN backbones. ADDA has a poor performance, probably for its unique separated source and target extractors, which reveals the significance of model sharing across domains. RevGrad shows no benefit for performance, and ENT is even harmful for cross-domain classification in general. CDAC performs badly in RS-SSDA probably because the high overlapping objects and features across RS scene classes make the unlabeled feature-similarity-based clustering strategy of CDAC invalid; this phenomenon can be intuitively observed in its feature distribution provided in Section From the perspective of specific adaptation scenarios, the results from these experiments demonstrate that our BSCA can achieve the best knowledge transfer on most of the scenarios, except some scenarios where WHU serves as the target domain. The reason may be that its small number of samples limits the effectiveness of cross-domain alignment as well as its further classification performance.In general, the experimental results demonstrate the effectiveness, superiority, and stability of the proposed BSCA when facing various RS adaptation scenarios and CNN backbones in comparison with SSL, UDA, and other state-of-the-art SSDA methods.4.5To intuitively show the feature alignment effect of the proposed BSCA, in The SSL method of FixMatch roughly aggregates the target features, but it ignores the utilization of the source features, resulting in the obvious cross-domain semantic gap. The UDA method of ECB-FAM decreases the cross-domain discrepancy, while the class boundary is not clear for some classes like “Residential” and “Industrial”. For SSDA methods, compared with the baseline of S+T, RevGrad shows little effect on reducing the domain shift, and the entropy minimization operation in ENT plays a negative role of making the cross-domain intra-class feature separated, which are consistent with its classification results. SSDAN works well by keeping the classes away from each other without interference to the cross-domain intra-class distribution. CDAC performs badly in RS-SSDA, probably influenced by its adaptive clustering method of unlabeled target feature based on the similarity of objects and feature covered in two images. However, there are plenty of object and feature overlapped among different RS scene classes, such as buildings and roads. As shown in 5In this paper, aiming at semi-supervised domain adaptation (SSDA) for remote sensing (RS) cross-domain scene classification, we design a bidirectional sample-class alignment (BSCA) method to reduce the semantic discrepancy between a source domain and a target domain. BSCA consists of two kinds of alignment mechanisms: unsupervised alignment (UA) and supervised alignment (SA). Firstly, the UA module concentrates on the inter-domain feature alignment in an unsupervised manner, not only between the source domain and the unlabeled target domain but also between the labeled target domain and the unlabeled target domain, which are achieved by decreasing their respective MMD distance. Then, the SA module aims to further reduce the domain shifts at the class level by the following sub-steps: calculate class-wise centers of two domains from the memory banks; make the sample-class feature alignment, both from source samples to their associated target class centers and from target samples from their associated source class centers; store the feature extracted from samples to the class-wise memory banks of two domains. In comparison with some state-of-the-art SSL/UDA/SSDA methods, our BSCA achieves superior classification performance on the unlabeled target domain with compact cross-domain intra-class feature representation and a low-entropy classification boundary. Such results demonstrate the effectiveness and robustness of the proposed method.In future work, considering the performance bottleneck resulting from incorrect pseudo-labeled target samples in our BSCA, we will focus on improving the quality of pseudo-labels via dynamic threshold techniques such as curriculum learning.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


The WxL domain is found on the cell surface of many bacteria, most of which are commensal gut bacteria. Its functions are generally identified as being related to virulence and/or peptidoglycan attachment, but there is so far no clear function or structure for this domain. Here, a range of bioinformatics tools were used to clarify the structure and function. These indicate that WxL domains occur in cell surface-associated gene clusters that always contain a small WxL, large WxL and DUF916 domain; and that the small and large WxL proteins have distinct structure despite sharing two conserved WxL motifs. The two WxL motifs form a hydrophobic surface buried inside the protein. The likely function of the WxL domain is to attach to bacterial peptidoglycan, forming a platform to allow associated domains in the cluster to interact with host proteins. These species are typically commensal gut bacteria, which cause opportunistic infections in immunocompromised individuals and are often nosocomial. WxL proteins are typically found in gene clusters, named Csc (Cell surface cluster) in Listeria , TargetP 2.0 (https://www.cbs.dtu.dk/services/TargetP/), cNLS Mapper (https://nls-mapper.iab.keio.ac.jp/cgi-bin/NLS_Mapper_form.cgi), TMHMM server v. 2.0 , HMMTOP (https://www.enzim.hu/hmmtop/html/submit.html) and Protter (https://wlab.ethz.ch/protter/start/). Interaction networks were identified using the STRING database (https://string-db.org) with a score cut-off value of 0.40.Physicochemical properties were determined with the ProtParam tool available from ExPASy 2.3https://www.ebi.ac.uk/interpro/result/InterProScan/), Conserved Domain Architecture Retrieval Tool (CDART) (https://www.ncbi.nlm.nih.gov/Structure/lexington/lexington.cgi),Domains, motifs and families were identified using InterProScan  atabases . CDART satabases . SMART iatabases . Sequenc/muscle/ .2.4https://alphafold.ebi.ac.uk  (33 WxL protein sequences were compared using the MUSCLE server (muscle/)  and are 3.2WxL domains are exclusively found in bacteria . AccordiOver 91 % of the 938 WxL sequences known in bacteria are found in Firmicutes, of which 99 % are in Bacilli. The WxL motif is widely distributed in two Bacilli orders: Lactobacillales (755) and Bacillalles (96) .3.3N-terminus and are therefore secreted proteins.As noted by previous authors , WxL proThe number, position and orientation of small and large WxL domains is not identical in all the species. Some species have one small WxL and one large WxL, while others have two small and one large WxL. It is of interest that all clusters contain a large WxL, a small WxL and a DUF916 domain, which therefore appears to be the minimal domain organisation. DUF916 is a poorly characterised Domain of Unknown Function.E. faecalis V583  is preceded by a FHL2-interacting domain (FID), starting at residue 607. Analysis using RosettaFold and AlphaFold shows that in fact the domain boundary is at residue 581, with the FID domain being a fimbrial adhesin homologue.It is worth adding that it has been proposed  that in 3.5Previous analyses of WxL domains were unable to model a structure . GallowaThe models for small WxL domains are highly superimposable , providiThe most interesting observation from these comparisons is that although the two WxL sequence motifs are well conserved across small and large WxL , and theFor both domains, the two WxL motifs are found on two adjacent β-strands. A detailed analysis of the structure predictions for the two domains demonstrates that they have more in common than just this sequence location, which provides clues as to the possible functions of the WxL motifs.1 + 4 and W2-2, and also by a hydrophobic residue at W1-10, which is contributed by the strand positioned above the WxL motif and further enlarges the hydrophobic core. The conserved residues Asp W1-8 and Arg W1-6 form a pair of hydrogen bonds between their sidechains that help to stabilise the start of a turn between the strand containing W1-10 and the first WxL strand, while the conserved GTxAG sequence following them forms the turn itself. Of particular interest is the N-terminal strand, shown in blue. This is in a similar place in both structures, despite the fact that in the topology diagrams it occupies quite different positions, being part of the upper β-sheet in LwpLB3, and part of the lower β-sheet in SwpE2. It is connected to the rest of the domain by a long sequence lacking regular secondary structure. We therefore hypothesise that the rationale for the conserved WxL motifs involves a structural rearrangement in this region, involving a rearrangement of the N-terminal strand, with the WxL platform forming a solid base for the structural change. The two pairs of WxL sidechains are buried in the structure and do not appear to play any role in ligand recognition or binding.The three-dimensional AlphaFold models for the large WxL domain from LwpLB3 and small WxL domain from SwpE2 are shown in 3.6was-michaelislab.org was used to identify potential ligands for the WxL domains (See N-acetyl glucosamine (NAG) as a likely ligand. NAG forms half of the peptidoglycan backbone, further confirming the likely role of the large WxL domain in binding to peptidoglycan. The binding site for NAG is at the back of the domain, behind the WxL motifs in the view shown in N-terminal strand are on a surface pointing away from the bacterial surface towards a potential host.The 3D Ligand site webserver ains See . For smaN-acetyl muramic acid) as possible ligands for both small and large WxL, with the binding sites being at similar locations to that described above.Docking was also conducted using the program MOE . This identified both NAG and NAM  assemble together, attached to peptidoglycan via small and large WxL domains. Small WxL protein contains no other domains and thus is presumably a core part of this complex. The other domains in the cluster (ie DUF3324 and other domains on large WxL) extend out, away from the bacterial surface, and are available for attachment to the host and also potentially for immune evasion.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Nautilus pompilius  and FcaMT1 of Falcidens caudatus (Caudofoveata class). Interestingly, whereas NpoMT1 consists of α and β1 domains and has a prototypical Cd2+ preference, FcaMT1 has a singular preference for Zn2+ ions and a distinct domain composition, including a new Caudofoveata-specific δ domain. Overall, our results suggest that the modular architecture of MTs has contributed to MT evolution during mollusk diversification, and exemplify how modularity increases MT evolvability.Protein domains are independent structural and functional modules that can rearrange to create new proteins. While the evolution of multidomain proteins through the shuffling of different preexisting domains has been well documented, the evolution of domain repeat proteins and the origin of new domains are less understood. Metallothioneins (MTs) provide a good case study considering that they consist of metal-binding domain repeats, some of them with a likely de novo origin. In mollusks, for instance, most MTs are bidomain proteins that arose by lineage-specific rearrangements between six putative domains: α, β1, β2, β3, γ and δ. Some domains have been characterized in bivalves and gastropods, but nothing is known about the MTs and their domains of other Mollusca classes. To fill this gap, we investigated the metal-binding features of NpoMT1 of  A protein domain is a well-defined region of a protein that constitutes a stable, independently folding, and compact structural unit that might perform a specific function ,2,3. ProMTs have classically been defined as low molecular weight (<100 amino acids) and cysteine-rich (≈15–30%) proteins that, because they bind transition metal ions and lack enzymatic functions, have been related with metal homeostasis and detoxification in living beings , and δ and β1 domains for the F. caudatus MT  x5[CxC]x3Cx4[CxC]x3[CxC]x3[CxC]x2C motifs, linked by three residues to a carboxyl-terminal β1 domain with its 9 Cys arranged in a pattern of [CxC]x3[CxC]x3Cx5[CxC]x3[CxC] motifs  after demetallation by acidification of the NpoMT1 and FcaMT1 productions were consistent with the theoretical masses   based on the study of metalated species in recombinantly produced metal–protein complexes . Thus, bctively) A,C, veri2+ ions -enriched culture medium yielded a unique metalated species of NpoMT1 coordinating seven Cd species B, sugges species . Based o8- and Cd8-FcaMT) was observed when FcaMT1 was synthesized in Zn2+- or Cd2+-enriched medium  and δ (from Met1 to Lys45) domains of NpoMT1 and FcaMT1, respectively, we followed the same strategy as for the full-length proteins. In brief, both putative domains were expressed as GST-fusion proteins in E. coli BL21 cultures supplemented with ZnCl2, CdCl2 or CuSO4, and metal–protein complexes were purified and characterized by ICP-AES and ESI-MS analyses. The experimental masses corresponding to the apo-α and apo-δ domains  were consistent with their theoretical masses   B,D, conf2+ ions  was observed when it was synthesized in Zn-enriched medium  and Caudofoveata F. caudatus (FcaMT1) species, and their distinctive α and δ domains. We determined the MT features of both MTs, establishing that NpoMT1 and its α domain render homometallic complexes with seven and four Cd2+ ions, respectively  + 3(β1) = 8Zn2+ ions for FcaMT1. These stoichiometries show a higher metal-binding capacity of FcaMT1 in comparison with NpoMT1 due to the two extra Cys at its N-terminal domain: 14 Cys in the δ domain versus 12 Cys in the α domain. Our results also indicate that the α and δ domains are functionally autonomous, and experimentally confirm the predicted bidomain αβ1 and δβ1 structures of Cephalopoda and Caudofoveata MTs, respectively.Metallothioneins are becoming a valuable model system to investigate the origin and evolution of protein domains for two main reasons. First, based on their patterns of Cys motifs, MT domains can be predicted in broad phylogenetic contexts, and thereby, the changes in their domain organization can be visualized along the evolutionary ladder. Second, standardized protocols of analytical techniques (ESI-MS and ICP-AES) allow the functional characterization of the predicted MT domains based on their metal-binding features, that is, their metal-binding preferences and capacities, exposing the functional consequences of the innovations in domain rearrangements. Several mollusk MTs and some domains had previously been functionally characterized in the Bivalvia and Gastropoda classes, but nothing was known about the MTs from the Cephalopoda class of the Conchifera clade, nor from any Caudofoveata MTs of the Aculifera clade. We have now characterized the putative MTs from Cephalopoda ectively A,G, wherectively E,K. Cons2+ ions for the α domain, and Zn2+ ions for the δ domain) indicating that the distinctive N-terminal domain is fundamental for the metal preference of the full-length proteins. From an evolutionary perspective, considering the ancient origin of the α domain and the pervasiveness of Cd-specific MTs in mollusks [F. caudatus lineage, and thereby, likely associated to the appearance of the δ domain in the Caudofoveata class. Because marine mollusk species typically have Cd-thioneins [F. caudatus, FcaMT2, made up of a “degenerated” δ-like domain  maintaining only 9 of the 14 Cys, fused to a conserved β1 domain   . WhetherHelix pomatia and Cornu aspersum. The chemical properties of these MTs agree with the biological findings on their organ- and cell-specific accumulation and distribution as well as with their presumed role in the metal metabolism of these two snail species [N. pompilius, results from other cephalopod species consistently indicate that cephalopods preferentially accumulate Cd in their midgut gland, where they can detoxify it by binding to “metallothionein-like proteins”, including metallothioneins [Nautilus macromphalus, for example, has been reported to accumulate huge amounts of many different metals in its digestive gland, which may serve as a storage and detoxification organ for most of them too, probably by binding to MTs and by intracellular compartmentalization [F. caudatus, are completely lacking. This is mainly because there are hardly any studies on metal accumulation in Caudofoveates. One reason for this is probably that the class Caudofoveata is small and most representatives of this class live burrowing in the seabed and in deep-sea sediments [In some cases, it has been possible to relate the chemical findings on the metal-binding properties of some MTs with their biological functions. This has been achieved, for instance, in the case of the Cd- and Cu-specific MT isoforms of the terrestrial snails  species . In the hioneins ,32,33. Tlization . Againstediments , which mBased on the phylogenetic distribution of the different MT domains, it is thought that the origin of α and β1 domains predated the diversification of the Mollusca phylum, while the δ domain would have evolved de novo, probably concurring with the appearance of the Caudofoveata class . De novoWe do not know the source of the new MT domains or proteins, but in the last years data have accumulated showing that short open reading frames (sORFs) in long non-coding RNAs (lncRNAs) might be an important source of de novo peptides ,42,43. TProtein modularity accelerates the evolutionary process of generating genetic diversity because it favors the domain shuffling, which is an effective means of exploring new viable sequences and creating new proteins as it has a greater impact on the functional properties of proteins than the progressive accumulation of point mutations in their sequences ,46. Modu2+ ions) that would have been selected depending on the species-specific adaptive requirements in the Caudofoveata lineage . Furthermore, the modular architecture of MTs would also have facilitated the duplications and losses of complete domains in a lineage-specific manner. For instance, losses of α and β domains occurred during the evolution of some mollusk lineages, including the Caudofoveata species, while these domains have been tandem duplicated in several gastropod and bivalve species, giving rise to large multidomain forms  with different metal-binding properties  and supplementation with ZnCl2 (300 μM), CdCl2 (300 μM) or CuSO4 (500 μM). Metal-MT complexes were purified from the soluble protein fraction of sonicated bacteria by affinity purification of the GST-tagged proteins, and digestion with thrombin. The metal-MT complexes were concentrated with a 3 kDa Centripep Low Concentrator , and fractionated on a Superdex-75 FPLC column  equilibrated with 20 mM Tris-HCl, pH 7.0 and run at 0.8 mL min−1. The protein-containing fractions, identified by their absorbance at 254 nm, were pooled and stored at −80 °C until use.Production and purification of recombinant metal-MT complexes of NpoMT1, FcaMT1, αNpoMT1 and δFcaMT1 domains were performed as described elsewhere ,50. In b1 to T44) and δFcaMT1 (from Met1 to K45), were characterized by means of mass spectrometry (ESI-MS) and spectroscopy (ICP-AES). An electrospray ionization mass spectrometry (ESI-MS) MicroTOF-Q Instrument  interfaced with a Series 1100 HPLC pump , was used to determine the molecular mass of the recombinant proteins. The instrument was calibrated with ESI-L Low Concentration Tuning Mix (Agilent Technologies) and the experimental conditions were set up as follows: injection of 10–20 μL of sample through a PEEK long tube (1–1.5 m × 0.18 mm i.d.) at 30–50 μL min−1; capillary-counterelectrode voltage, 3.5–5.0 kV; desolvation temperature, 90–110 °C; dry gas, 6 L min−1. Data were acquired over an m/z range of 800 to 3000. The liquid carriers were a 90:10 mixture of 15 mM ammonium acetate and acetonitrile at pH 7.0 and a 95:5 mixture of formic acid and acetonitrile at pH 2.4.All designed constructs, NpoMT1, FcaMT1, αNpoMT1  by means of a PerkinElmer Optima 4300DV  at the correct wavelength  under conventional conditions . MTs’ co