Improving thermostability of Bacillus amyloliquefaciens alpha-amylase by multipoint mutations.

The medium-temperature alpha-amylase of Bacillus amyloliquefaciens is widely used in the food and washing process. Enhancing the thermostability of alpha-amylases and investigating the mechanism of stability are important for enzyme industry development. The optimal temperature and pH of the wild-type BAA and mutant MuBAA (D28E/V118A/S187D/K370 N) were all 60 °C and 6.0, respectively. The mutant MuBAA showed better thermostability at 50 °C and 60 °C, with a specific activity of 206.61 U/mg, which was 99.1% greater than that of the wild-type. By analyzing predicted structures, the improving thermostability of the mutant MuBAA was mainly related to enhanced stabilization of a loop region in domain B via more calcium-binding sites and intramolecular interactions around Asp187. Furthermore, additional intramolecular interactions around sites 28 and 370 in domain A were also beneficial for improving thermostability. Additionally, the decrease of steric hindrance at the active cavity increased the specific activity of the mutant MuBAA. Improving the thermostability of BAA has theoretical reference values for the modification of alpha-amylases.

Computational identification of ubiquitination sites in Arabidopsis thaliana using convolutional neural networks.

KEY MESSAGE: We developed two CNNs for predicting ubiquitination sites in Arabidopsis thaliana, demonstrated their competitive performance, analyzed amino acid physicochemical properties and the CNN structures, and predicted ubiquitination sites in Arabidopsis. As an important posttranslational protein modification, ubiquitination plays critical roles in plant physiology, including plant growth and development, biotic and abiotic stress, metabolism, and so on. A lot of ubiquitination site prediction models have been developed for human, mouse and yeast. However, there are few models to predict ubiquitination sites for the plant Arabidopsis thaliana. Based on this context, we proposed two convolutional neural network (CNN) based models for predicting ubiquitination sites in A. thaliana. The two models reach AUC (area under the ROC curve) values of 0.924 and 0.913 respectively in five-fold cross-validation, and 0.921 and 0.914 respectively in independent test, which outperform other models and demonstrate the competitive edge of them. We in-depth analyze the amino acid physicochemical properties in the neighboring sequence regions of the ubiquitination sites, and study the influence of the CNN structure to the prediction performance. Potential ubiquitination sites in the global Arbidopsis proteome are predicted using the two CNN models. To facilitate the community, the source code, training and test dataset, predicted ubiquitination sites in the Arbidopsis proteome are available at GitHub ( http://github.com/nongdaxiaofeng/CNNAthUbi ) for interest users.

Expression and characterization of a chitinase from Serratia marcescens.

A chitinase gene from Serratia marcescens was cloned and expressed in Escherichia coli BL21(DE3) and the properties of recombinant chitinase rCHI-2 were characterized. The optimum catalytic pH of rCHI-2 was 6.0. It was stable in the pH range of 6.0-9.0 and could maintain more than 90% of its relative enzyme activity after incubation at 37 °C for 1 h. The optimum catalytic temperature of the enzyme was 55 °C and 85% of enzyme activity was remained after incubation at 45 °C for 1 h. The activation energy of the thermal inactivation of the enzyme was 10.9 kJ/mol and the Michaelis-Menten constant was 3.2 g/L. The purified rCHI-2 was found to be highly stable at 45 °C with half-life (t1/2) of 289 min and thermodynamic parameters ΔH*, ΔG* and ΔS* revealed high affinity of rCHI-2 for chitin. Hg2+ was found to be able to inhibit the enzyme activity reversibly, while IC50 and inhibition constant of Hg2+ on the enzyme were 34.8 µmol/L and 44.6 µmol/L, respectively. Moreover, rCHI-2 could specifically hydrolyze colloidal chitin into GlcNAc2 as the major product.

RFAthM6A: a new tool for predicting m6A sites in Arabidopsis thaliana.

KEY MESSAGE: We curated a reliable dataset of m6A sites in Arabidopsis thaliana, built competitive models for predicting m6A sites, extracted predominant rules from the prediction models and analyzed the most important features. In biological RNA, approximately 150 chemical modifications have been discovered, of which N6-methyladenine (m6A) is the most prevalent and abundant. This modification plays an essential role in a myriad of biological mechanisms and regulates RNA localization, nuclear export, translation, stability, alternative splicing, and other processes. However, m6A-seq and other wet-lab techniques do not easily facilitate accurate and complete determination of m6A sites across the transcriptome. Therefore, the use of computational methods to establish accurate models for predicting m6A sites is essential. In this work, we manually curated a reliable dataset of m6A sites and non-m6A sites and developed a new tool called RFAthM6A for predicting m6A sites in Arabidopsis thaliana. Briefly, RFAthM6A consists of four independent models named RFPSNSP, RFPSDSP, RFKSNPF and RFKNF and strict benchmarks show that the AUC values of the four models reached 0.894, 0.914, 0.920 and 0.926, respectively in a fivefold cross validation and the prediction performance of RFPSDSP, RFKSNPF and RFKNF exceeded that of three previously reported models (AthMethPre, M6ATH and RAM-NPPS). Linear combination of the prediction scores of RFPSDSP, RFKSNPF and RFKNF improved the prediction performance. We also extracted several predominant rules that underlie the m6A site identification from the trained models. Furthermore, the most important features of the predictors for the m6A site identification were also analyzed in depth. To facilitate use of our proposed models by interested researchers, all the source codes and datasets are publicly deposited at https://github.com/nongdaxiaofeng/RFAthM6A .

PlantNh-Kcr: a deep learning model for predicting non-histone crotonylation sites in plants.

BACKGROUND: Lysine crotonylation (Kcr) is a crucial protein post-translational modification found in histone and non-histone proteins. It plays a pivotal role in regulating diverse biological processes in both animals and plants, including gene transcription and replication, cell metabolism and differentiation, as well as photosynthesis. Despite the significance of Kcr, detection of Kcr sites through biological experiments is often time-consuming, expensive, and only a fraction of crotonylated peptides can be identified. This reality highlights the need for efficient and rapid prediction of Kcr sites through computational methods. Currently, several machine learning models exist for predicting Kcr sites in humans, yet models tailored for plants are rare. Furthermore, no downloadable Kcr site predictors or datasets have been developed specifically for plants. To address this gap, it is imperative to integrate existing Kcr sites detected in plant experiments and establish a dedicated computational model for plants. RESULTS: Most plant Kcr sites are located on non-histones. In this study, we collected non-histone Kcr sites from five plants, including wheat, tabacum, rice, peanut, and papaya. We then conducted a comprehensive analysis of the amino acid distribution surrounding these sites. To develop a predictive model for plant non-histone Kcr sites, we combined a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and attention mechanism to build a deep learning model called PlantNh-Kcr. On both five-fold cross-validation and independent tests, PlantNh-Kcr outperformed multiple conventional machine learning models and other deep learning models. Furthermore, we conducted an analysis of species-specific effect on the PlantNh-Kcr model and found that a general model trained using data from multiple species outperforms species-specific models. CONCLUSION: PlantNh-Kcr represents a valuable tool for predicting plant non-histone Kcr sites. We expect that this model will aid in addressing key challenges and tasks in the study of plant crotonylation sites.

Outer membrane proteins can be simply identified using secondary structure element alignment.

BACKGROUND: Outer membrane proteins (OMPs) are frequently found in the outer membranes of gram-negative bacteria, mitochondria and chloroplasts and have been found to play diverse functional roles. Computational discrimination of OMPs from globular proteins and other types of membrane proteins is helpful to accelerate new genome annotation and drug discovery. RESULTS: Based on the observation that almost all OMPs consist of antiparallel ß-strands in a barrel shape and that their secondary structure arrangements differ from those of other types of proteins, we propose a simple method called SSEA-OMP to identify OMPs using secondary structure element alignment. Through intensive benchmark experiments, the proposed SSEA-OMP method is better than some well-established OMP detection methods. CONCLUSIONS: The major advantage of SSEA-OMP is its good prediction performance considering its simplicity. The web server implements the method is freely accessible at http://protein.cau.edu.cn/SSEA-OMP/index.html.

Computational identification of human ubiquitination sites using convolutional and recurrent neural networks.

Ubiquitination is a very important protein post-translational modification in humans, which is closely related to many human diseases such as cancers. Although some methods have been elegantly proposed to predict human ubiquitination sites, the accuracy of these methods is generally not very satisfactory. In order to improve the prediction accuracy of human ubiquitination sites, we propose a new ensemble method HUbipPred, which takes the binary encoding and physicochemical properties of amino acids as training features, and integrates two intensively trained convolutional neural networks and two recurrent neural networks to build the model. Finally, HUbiPred achieves AUC values of 0.852 and 0.844 in five-fold cross-validation and independent tests, respectively, which greatly improves the prediction accuracy compared to previous predictors. We also analyze the physicochemical properties of amino acids around ubiquitination sites, study the important roles of architectures (i.e., convolution, long short-term memory (LSTM) and fully connected hidden layers) in the networks for prediction performance, and also predict potential ubiquitination sites in humans using HUbiPred. The training and test datasets, predicted human ubiquitination sites, and source codes of HUbiPred are publicly available at https://github.com/amituofo-xf/HUbiPred.

DDAPRED: a computational method for predicting drug repositioning using regularized logistic matrix factorization.

Due to rising development costs and stagnant product outputs of traditional drug discovery methods, drug repositioning, which discovers new indications for existing drugs, has attracted increasing interest. Computational drug repositioning can integrate prioritization information and accelerate time lines even further. However, most existing methods for predicting drug repositioning have low precisions. The present article proposed a new method named DDAPRED (https://github.com/nongdaxiaofeng/DDAPRED) for drug repositioning prediction. The method integrated multiple sources of drug similarity and disease similarity information, and it used the regularized logistic matrix decomposition method to significantly improve the prediction performance. In 5-fold cross-validation, the areas under the receiver operating characteristic curve (AUROC) and the precision-recall curve (AUPRC) of DDAPRED reached 0.932 and 0.438, respectively, exceeding other methods. The present study also analyzed the parameters influencing the model performance and the effect of different drug similarity information in-depth, and it verified the treatment relationship of the top 50 predictions with unknown relationships in the training set, further demonstrating the practicability of our method.

DescFold: a web server for protein fold recognition.

BACKGROUND: Machine learning-based methods have been proven to be powerful in developing new fold recognition tools. In our previous work [Zhang, Kochhar and Grigorov (2005) Protein Science, 14: 431-444], a machine learning-based method called DescFold was established by using Support Vector Machines (SVMs) to combine the following four descriptors: a profile-sequence-alignment-based descriptor using Psi-blast e-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blast e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. In this work, we focus on the improvement of DescFold by incorporating more powerful descriptors and setting up a user-friendly web server. RESULTS: In seeking more powerful descriptors, the profile-profile alignment score generated from the COMPASS algorithm was first considered as a new descriptor (i.e., PPA). When considering a profile-profile alignment between two proteins in the context of fold recognition, one protein is regarded as a template (i.e., its 3D structure is known). Instead of a sequence profile derived from a Psi-blast search, a structure-seeded profile for the template protein was generated by searching its structural neighbors with the assistance of the TM-align structural alignment algorithm. Moreover, the COMPASS algorithm was used again to derive a profile-structural-profile-alignment-based descriptor (i.e., PSPA). We trained and tested the new DescFold in a total of 1,835 highly diverse proteins extracted from the SCOP 1.73 version. When the PPA and PSPA descriptors were introduced, the new DescFold boosts the performance of fold recognition substantially. Using the SCOP_1.73_40% dataset as the fold library, the DescFold web server based on the trained SVM models was further constructed. To provide a large-scale test for the new DescFold, a stringent test set of 1,866 proteins were selected from the SCOP 1.75 version. At a less than 5% false positive rate control, the new DescFold is able to correctly recognize structural homologs at the fold level for nearly 46% test proteins. Additionally, we also benchmarked the DescFold method against several well-established fold recognition algorithms through the LiveBench targets and Lindahl dataset. CONCLUSIONS: The new DescFold method was intensively benchmarked to have very competitive performance compared with some well-established fold recognition methods, suggesting that it can serve as a useful tool to assist in template-based protein structure prediction. The DescFold server is freely accessible at http://202.112.170.199/DescFold/index.html.

TIM-Finder: a new method for identifying TIM-barrel proteins.

BACKGROUND: The triosephosphate isomerase (TIM)-barrel fold occurs frequently in the proteomes of different organisms, and the known TIM-barrel proteins have been found to play diverse functional roles. To accelerate the exploration of the sequence-structure protein landscape in the TIM-barrel fold, a computational tool that allows sensitive detection of TIM-barrel proteins is required. RESULTS: To develop a new TIM-barrel protein identification method in this work, we consider three descriptors: a sequence-alignment-based descriptor using PSI-BLAST e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. With the assistance of Support Vector Machine (SVM), the three descriptors were combined to obtain a new method with improved performance, which we call TIM-Finder. When tested on the whole proteome of Bacillus subtilis, TIM-Finder is able to detect 194 TIM-barrel proteins at a 99% confidence level, outperforming the PSI-BLAST search as well as one existing fold recognition method. CONCLUSIONS: TIM-Finder can serve as a competitive tool for proteome-wide TIM-barrel protein identification. The TIM-Finder web server is freely accessible at http://202.112.170.199/TIM-Finder/.

Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods.

The zinc (Zn2+) cofactor has been proven to be involved in numerous biological mechanisms and the zinc-binding site is recognized as one of the most important post-translation modifications in proteins. Therefore, accurate knowledge of zinc ions in protein structures can provide potential clues for elucidation of protein folding and functions. However, determining zinc-binding residues by experimental means is usually lab-intensive and associated with high cost in most cases. In this context, the development of computational tools for identifying zinc-binding sites is highly desired, especially in the current post-genomic era. In this work, we developed a novel zinc-binding site prediction method by combining several intensively-trained machine learning models. To establish an accurate and generative method, we downloaded all zinc-binding proteins from the Protein Data Bank and prepared a non-redundant dataset. Meanwhile, a well-prepared dataset by other groups was also used. Then, effective and complementary features were extracted from sequences and three-dimensional structures of these proteins. Moreover, several well-designed machine learning models were intensively trained to construct accurate models. To assess the performance, the obtained predictors were stringently benchmarked using the diverse zinc-binding sites. Furthermore, several state-of-the-art in silico methods developed specifically for zinc-binding sites were also evaluated and compared. The results confirmed that our method is very competitive in real world applications and could become a complementary tool to wet lab experiments. To facilitate research in the community, a web server and stand-alone program implementing our method were constructed and are publicly available at . The downloadable program of our method can be easily used for the high-throughput screening of potential zinc-binding sites across proteomes.

Mutagenesis on the surface of a ß-agarase from Vibrio sp. ZC-1 increased its thermo-stability.

The recombinant rAgaZC-1 was a family GH50 ß-agarase from Vibrio sp. ZC-1 (CICC 24670). In this paper, the mutant D622G (i.e., mutate the aspartic acid at position 622 to glycine) had better thermo-stability than rAgaZC-1, showing 1.5℃ higher T5010 (the temperature at which the half-time is 10 min) and 4-folds of half-time at 41℃, while they had almost same optimum temperature (38.5℃), optimum pH (pH6.0) and catalytic efficiency. Thermal deactivation kinetical analysis showed that D622G had higher activation energy for deactivation, enthalpy and Gibbs free energy than rAgaZC-1, indicating that more energy is required by D622G for deactivation. Substrate can protect agarase against thermal inactivation, especially D622G. Hence the yield of agarose hydrolysis catalyzed by D622G was higher than that by rAgaZC-1. The models of D622G and rAgaZC-1 predicted by homology modeling were compared to find that it is the improved distribution of surface electrostatic potential, great symmetric positive potential and more hydrophobic interactions of D622G that enhance the thermo-stability.

A New Extremely Halophilic, Calcium-Independent and Surfactant-Resistant Alpha-Amylase from Alkalibacterium sp. SL3.

A new α-amylase-encoding gene (amySL3) of glycoside hydrolase (GH) family 13 was identified in soda lake isolate Alkalibacterium sp. SL3. The deduced AmySL3 shares high identities (82-98%) with putative α-amylases from the genus Alkalibacterium, but has low identities (<53%) with functionally characterized counterparts. amySL3 was successfully expressed in Escherichia coli, and the recombinant enzyme (rAmySL3) was purified to electrophoretic homogeneity. The optimal temperature and pH of the activity of the purified rAmySL3 were determined to be 45°C and pH 7.5, respectively. rAmySL3 was found to be extremely halophilic, showing maximal enzyme activity at a nearly saturated concentration of NaCl. Its thermostability was greatly enhanced in the presence of 4 M NaCl, and it was highly stable in 5 M NaCl. Moreover, the enzyme did not require calcium ions for activity, and was strongly resistant to a range of surfactants and hydrophobic organic solvents. The major hydrolysis products of rAmySL3 from soluble starch were maltobiose and maltotriose. The high ratio of acidic amino acids and highly negative electrostatic potential surface might account for the halophilic nature of AmySL3. The extremely halophilic, calcium-independent, and surfactant-resistant properties make AmySL3 a promising candidate enzyme for both basic research and industrial applications.

Truncation of κ­carrageenase for higher κ­carrageenan oligosaccharides yield with improved enzymatic characteristics.

Carrageenase is useful for preparation of carrageenan oligosaccharides, which have significant bioactivity. We expressed a κ­carrageenase gene from Zobellia sp. ZL-4 in full-length (κ-ZL-4) or after truncation of the carbohydrate binding module and the Type-IX secretion module (κ-ZL-4-GH16). κ-ZL-4-GH16 showed a specific activity (134.22 U/mg) 1.93 times higher than that of κ-ZL-4, and its thermal and pH stability also increased. The best activity of κ-ZL-4-GH16 was presented at pH 3.0-6.0, which was lower than the optimal pH of reported κ-carrageenases. The enzyme-substrate affinity of κ-ZL-4-GH16 was higher than that of κ-ZL-4, demonstrated by its lower Michaelis-Menten constant (0.704 mg/mL at pH 6.0). Importantly, κ-ZL-4-GH16 released 10-fold more κ-carrageenan disaccharides than κ-ZL-4. The κ-carrageenan tetrose and hexose produced by the two enzymes were purified and structurally identified. Molecular docking with κ-carrageenan hexose suggested that the efficiency improvement after truncation might be attributed to the conformation differences of the two enzymes.

Novel Salt-Tolerant Xylanase from a Mangrove-Isolated Fungus Phoma sp. MF13 and Its Application in Chinese Steamed Bread.

A novel glycosyl hydrolase family 11 xylanase gene, xynMF13A, was cloned from Phoma sp. MF13, a xylanase-producing fungus isolated from mangrove sediment. xynMF13A was heterologously expressed in Pichia pastoris, and the recombinant XynMF13A (rXynMF13A) was purified by Ni-affinity chromatography. The temperature and pH optima of purified rXynMF13A were 45 °C and pH 5.0, respectively. rXynMF13A showed a high level of salt tolerance, with maximal enzyme activity being seen at 0.5 M NaCl and as much as 53% of maximal activity at 4 M NaCl. The major rXynMF13A hydrolysis products from corncob xylan were xylobiose, xylotriose, xylotetraose, and xylopentaose, but no xylose was found. These hydrolysis products suggest an important potential for XynMF13A in the production of xylooligosaccharides (XOs). Furthermore, rXynMF13A had beneficial effects on Chinese steamed bread production, by increasing specific volume and elasticity while decreasing hardness and chewiness. These results demonstrate XynMF13A to be a novel xylanase with potentially significant applications in baking, XOs production, and seafood processing.

DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites.

Protein dephosphorylation, which is an inverse process of phosphorylation, plays a crucial role in a myriad of cellular processes, including mitotic cycle, proliferation, differentiation, and cell growth. Compared with tyrosine kinase substrate and phosphorylation site prediction, there is a paucity of studies focusing on computational methods of predicting protein tyrosine phosphatase substrates and dephosphorylation sites. In this work, we developed two elegant models for predicting the substrate dephosphorylation sites of three specific phosphatases, namely, PTP1B, SHP-1, and SHP-2. The first predictor is called MGPS-DEPHOS, which is modified from the GPS (Group-based Prediction System) algorithm with an interpretable capability. The second predictor is called CKSAAP-DEPHOS, which is built through the combination of support vector machine (SVM) and the composition of k-spaced amino acid pairs (CKSAAP) encoding scheme. Benchmarking experiments using jackknife cross validation and 30 repeats of 5-fold cross validation tests show that MGPS-DEPHOS and CKSAAP-DEPHOS achieved AUC values of 0.921, 0.914 and 0.912, for predicting dephosphorylation sites of the three phosphatases PTP1B, SHP-1, and SHP-2, respectively. Both methods outperformed the previously developed kNN-DEPHOS algorithm. In addition, a web server implementing our algorithms is publicly available at http://genomics.fzu.edu.cn/dephossite/ for the research community.

SOHPRED: a new bioinformatics tool for the characterization and prediction of human S-sulfenylation sites.

Protein S-sulfenylation (SOH) is a type of post-translational modification through the oxidation of cysteine thiols to sulfenic acids. It acts as a redox switch to modulate versatile cellular processes and plays important roles in signal transduction, protein folding and enzymatic catalysis. Reversible SOH is also a key component for maintaining redox homeostasis and has been implicated in a variety of human diseases, such as cancer, diabetes, and atherosclerosis, due to redox imbalance. Despite its significance, the in situ trapping of the entire 'sulfenome' remains a major challenge. Yang et al. have recently experimentally identified about 1000 SOH sites, providing an enriched benchmark SOH dataset. In this work, we developed a new ensemble learning tool SOHPRED for identifying protein SOH sites based on the compositions of enriched amino acids and the physicochemical properties of residues surrounding SOH sites. SOHPRED was built based on four complementary predictors, i.e. a naive Bayesian predictor, a random forest predictor and two support vector machine predictors, whose training features are, respectively, amino acid occurrences, physicochemical properties, frequencies of k-spaced amino acid pairs and sequence profiles. Benchmarking experiments on the 5-fold cross validation and independent tests show that SOHPRED achieved AUC values of 0.784 and 0.799, respectively, which outperforms several previously developed tools. As a real application of SOHPRED, we predicted potential SOH sites for 193 S-sulfenylated substrates, which had been experimentally detected through a global sulfenome profiling in living cells, though the actual SOH sites were not determined. The web server of SOHPRED has been made publicly available at for the wider research community. The source codes and the benchmark datasets can be downloaded from the website.

A Novel Multi-domain High Molecular, Salt-Stable Alkaline Xylanase from Alkalibacterium sp. SL3.

A novel multi-domain high molecular xylanase coding gene (xynSL3) was cloned from Alkalibacterium sp. SL3, an alkaliphilic bacterial strain isolated from the sediment of soda lake Dabusu. The deduced XynSL3 is composed of a putative signal peptide, three tandem domains of carbohydrate binding module (CBM) family 22, a catalytic domain of glycosyl hydrolase (GH) family 10 and a domain of CBM9. XynSL3 shares the highest identity of 66% to a hypothetical protein from Alkalibacterium sp. AK22 and has low identities (33-45%) with other functionally characterized xylanases. The gene xynSL3 was expressed heterologously in Escherichia coli and the recombinant enzyme demonstrated some particular characteristics. Purified recombinant XynSL3 (rXynSL3) was highly active and stable over the neutral and alkaline pH ranges from 7.0 to 12.0, with maximum activity at pH 9.0 and around 45% activity at pH 12.0. It had an apparent temperature optimum of 55°C and was stable at 50°C. The rXynSL3 was highly halotolerant, retaining more than 60% activity with 3 M NaCl and was stable at up to a 4 M concentration of NaCl. The hydrolysis products of rXynSL3 from corncob xylan were mainly xylobiose and xylotetraose. The activity of rXynSL3 was enhanced by Ca2+ and it has strong resistance to sodium dodecyl sulfate (SDS). This multi-domain, alkaline and salt-tolerant enzyme has great potential for basic research and industrial applications such as the biobleaching of paper pulp and production of xylo-oligosaccharides (XOS).

A novel cold-adapted and highly salt-tolerant esterase from Alkalibacterium sp. SL3 from the sediment of a soda lake.

A novel esterase gene (estSL3) was cloned from the Alkalibacterium sp. SL3, which was isolated from the sediment of soda lake Dabusu. The 636-bp full-length gene encodes a polypeptide of 211 amino acid residues that is closely related with putative GDSL family lipases from Alkalibacterium and Enterococcus. The gene was successfully expressed in E. coli, and the recombinant protein (rEstSL3) was purified to electrophoretic homogeneity and characterized. rEstSL3 exhibited the highest activity towards pNP-acetate and had no activity towards pNP-esters with acyl chains longer than C8. The enzyme was highly cold-adapted, showing an apparent temperature optimum of 30 °C and remaining approximately 70% of the activity at 0 °C. It was active and stable over the pH range from 7 to 10, and highly salt-tolerant up to 5 M NaCl. Moreover, rEstSL3 was strongly resistant to most tested metal ions, chemical reagents, detergents and organic solvents. Amino acid composition analysis indicated that EstSL3 had fewer proline residues, hydrogen bonds and salt bridges than mesophilic and thermophilic counterparts, but more acidic amino acids and less hydrophobic amino acids when compared with other salt-tolerant esterases. The cold active, salt-tolerant and chemical-resistant properties make it a promising enzyme for basic research and industrial applications.