Multiple Protein Sequence Alignment Research Articles

The conserved cysteines at position 18, 36, and 49 of Autographa californica multiple nucleopolyhedrovirus VP39 are essential for virus replication.

Autographa californica nucleopolyhedrovirus orf89 (vp39) encodes the major capsid protein VP39. Multiple alignments of protein sequences showed that VP39 has 8 conserved cysteine (Cys) residues. Cysteine residues play an important role in proper function of a protein. To determine the importance of these conserved cysteine residues for virus proliferation, a series of recombinant viruses harboring VP39-Cys mutants were constructed. Viral growth curves and transmission electron microscopy showed that mutation of Cys29, Cys132, Cys169, Cys229, or Cys232 of VP39 to alanine did not affect budded virion production; however, the mutation of Cys18, Cys36, or Cys49 to alanine resulted in interruption of capsid assembly. Co-immunoprecipitation assays showed that mutations of these 8 cysteines individually or simultaneously had no effect on self-association of VP39. Immunofluorescence analysis by confocal microscopy revealed that the subcellular localization of VP39 with mutations in Cys18, Cys36 or Cys49 was exclusively distributed in the cytoplasm of a cell regardless of virus infection or not, while the wild-type VP39 or the VP39 carrying mutations in Cys29, Cys132, Cys169, Cys229, or Cys232 was distributed throughout the cytoplasm and the nucleus. Our results demonstrated that Cys18, Cys36, and Cys49 are essential for the proper localization of VP39, which is a prerequisite for successful nucleocapsid assembly of the virus.

Large-scale structure-informed multiple sequence alignment of proteins with SIMSApiper.

SIMSApiper is a Nextflow pipeline that creates reliable, structure-informed MSAs of thousands of protein sequences faster than standard structure-based alignment methods. Structural information can be provided by the user or collected by the pipeline from online resources. Parallelization with sequence identity-based subsets can be activated to significantly speed up the alignment process. Finally, the number of gaps in the final alignment can be reduced by leveraging the position of conserved secondary structure elements. The pipeline is implemented using Nextflow, Python3, and Bash. It is publicly available on github.com/Bio2Byte/simsapiper.

Associating protein sequence positions with the modulation of quantitative phenotypes

Although protein sequences encode the information for folding and function, understanding their link is not an easy task. Unluckily, the prediction of how specific amino acids contribute to these features is still considerably impaired. Here, we developed a simple algorithm that finds positions in a protein sequence with potential to modulate the studied quantitative phenotypes. From a few hundred protein sequences, we perform multiple sequence alignments, obtain the per-position pairwise differences for both the sequence and the observed phenotypes, and calculate the correlation between these last two quantities. We tested our methodology with four cases: archaeal Adenylate Kinases and the organisms optimal growth temperatures, microbial rhodopsins and their maximal absorption wavelengths, mammalian myoglobins and their muscular concentration, and inhibition of HIV protease clinical isolates by two different molecules. We found from 3 to 10 positions tightly associated with those phenotypes, depending on the studied case. We showed that these correlations appear using individual positions but an improvement is achieved when the most correlated positions are jointly analyzed. Noteworthy, we performed phenotype predictions using a simple linear model that links per-position divergences and differences in the observed phenotypes. Predictions are comparable to the state-of-art methodologies which, in most of the cases, are far more complex. All of the calculations are obtained at a very low information cost since the only input needed is a multiple sequence alignment of protein sequences with their associated quantitative phenotypes. The diversity of the explored systems makes our work a valuable tool to find sequence determinants of biological activity modulation and to predict various functional features for uncharacterized members of a protein family.

Rapid multiple protein sequence search by parallel and heterogeneous computation.

Protein sequence database search and multiple sequence alignment generation is a fundamental task in many bioinformatics analyses. As the data volume of sequences continues to grow rapidly, there is an increasing need for efficient and scalable multiple sequence query algorithms for super-large databases without expensive time and computational costs. We introduce Chorus, a novel protein sequence query system that leverages parallel model and heterogeneous computation architecture to enable users to query thousands of protein sequences concurrently against large protein databases on a desktop workstation. Chorus achieves over 100× speedup over BLASTP without sacrificing sensitivity. We demonstrate the utility of Chorus through a case study of analyzing a ∼1.5-TB large-scale metagenomic datasets for novel CRISPR-Cas protein discovery within 30 min. Chorus is open-source and its code repository is available at https://github.com/Bio-Acc/Chorus.

Decrypting orphan GPCR drug discovery via multitask learning

The drug discovery of G protein-coupled receptors (GPCRs) superfamily using computational models is often limited by the availability of protein three-dimensional (3D) structures and chemicals with experimentally measured bioactivities. Orphan GPCRs without known ligands further complicate the process. To enable drug discovery for human orphan GPCRs, multitask models were proposed for predicting half maximal effective concentrations (EC50) of the pairs of chemicals and GPCRs. Protein multiple sequence alignment features, and physicochemical properties and fingerprints of chemicals were utilized to encode the protein and chemical information, respectively. The protein features enabled the transfer of data-rich GPCRs to orphan receptors and the transferability based on the similarity of protein features. The final model was trained using both agonist and antagonist data from 200 GPCRs and showed an excellent mean squared error (MSE) of 0.24 in the validation dataset. An independent test using the orphan dataset consisting of 16 receptors associated with less than 8 bioactivities showed a reasonably good MSE of 1.51 that can be further improved to 0.53 by considering the transferability based on protein features. The informative features were identified and mapped to corresponding 3D structures to gain insights into the mechanism of GPCR-ligand interactions across the GPCR family. The proposed method provides a novel perspective on learning ligand bioactivity within the diverse human GPCR superfamily and can potentially accelerate the discovery of therapeutic agents for orphan GPCRs.

The Complex GH32 Enzyme Orchestra from Priestia megaterium Holds the Key to Better Discriminate Sucrose-6-phosphate Hydrolases from Other β-Fructofuranosidases in Bacteria.

Inulin is widely used as a prebiotic and emerging as a priming compound to counteract plant diseases. We isolated inulin-degrading strains from the lettuce phyllosphere, identified as Bacillus subtilis and Priestia megaterium, species hosting well-known biocontrol organisms. To better understand their varying inulin degradation strategies, three intracellular β-fructofuranosidases from P. megaterium NBRC15308 were characterized after expression in Escherichia coli: a predicted sucrose-6-phosphate (Suc6P) hydrolase (SacAP1, supported by molecular docking), an exofructanase (SacAP2), and an invertase (SacAP3). Based on protein multiple sequence and structure alignments of bacterial glycoside hydrolase family 32 enzymes, we identified conserved residues predicted to be involved in binding phosphorylated (Suc6P hydrolases) or nonphosphorylated substrates (invertases and fructanases). Suc6P hydrolases feature positively charged residues near the structural catalytic pocket (histidine, arginine, or lysine), whereas other β-fructofuranosidases contain tryptophans. This correlates with our phylogenetic tree, grouping all predicted Suc6P hydrolases in a clan associated with genomic regions coding for transporters involved in substrate phosphorylation. These results will help to discriminate between Suc6P hydrolases and other β-fructofuranosidases in future studies and to better understand the interaction of B. subtilis and P. megaterium endophytes with sucrose and/or fructans, sugars naturally present in plants or exogenously applied in the context of defense priming.

Simulations of Sequence Evolution: How (Un)realistic They Are and Why.

Simulating multiple sequence alignments (MSAs) using probabilistic models of sequence evolution plays an important role in the evaluation of phylogenetic inference tools and is crucial to the development of novel learning-based approaches for phylogenetic reconstruction, for instance, neural networks. These models and the resulting simulated data need to be as realistic as possible to be indicative of the performance of the developed tools on empirical data and to ensure that neural networks trained on simulations perform well on empirical data. Over the years, numerous models of evolution have been published with the goal to represent as faithfully as possible the sequence evolution process and thus simulate empirical-like data. In this study, we simulated DNA and protein MSAs under increasingly complex models of evolution with and without insertion/deletion (indel) events using a state-of-the-art sequence simulator. We assessed their realism by quantifying how accurately supervised learning methods are able to predict whether a given MSA is simulated or empirical. Our results show that we can distinguish between empirical and simulated MSAs with high accuracy using two distinct and independently developed classification approaches across all tested models of sequence evolution. Our findings suggest that the current state-of-the-art models fail to accurately replicate several aspects of empirical MSAs, including site-wise rates as well as amino acid and nucleotide composition.

AttCON: With better MSAs and attention mechanism for accurate protein contact map prediction

Protein contact map prediction is a critical and vital step in protein structure prediction, and its accuracy is highly contingent upon the feature representations of protein sequence information and the efficacy of deep learning models. In this paper, we propose an algorithm, DeepMSA+, to generate protein multiple sequence alignments (MSAs) and to construct feature representations based on co-evolutionary information and sequence information derived from MSAs. We also propose an improved deep learning model, AttCON, for training input features to predict protein contact map. The model incorporates an attention module, and by comparing different attention modules, we find a parameter-free attention module suitable for contact map prediction. Additionally, we use the Focal Loss function to better address the data imbalance issue in protein contact map. We also developed a weighted evaluation index (W score) for model evaluation, which takes into account a wide range of metrics. W score is comprehensive in its scope, with a particular focus on the precision of predictions for medium-range and long-range contacts. Experimental results show that AttCON achieves good precision results on datasets from CASP11 to CASP15. Compared to some state-of-the-art methods, it achieves an average improvement of over 5% in both medium-range and long-range predictions, and W score is improved by an average of 2 points.

ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design.

Deep generative models (DGMs) have shown great success in the understanding and data-driven design of proteins. Variational autoencoders (VAEs) are a popular DGM approach that can learn the correlated patterns of amino acid mutations within a multiple sequence alignment (MSA) of protein sequences and distill this information into a low-dimensional latent space to expose phylogenetic and functional relationships and guide generative protein design. Autoregressive (AR) models are another popular DGM approach that typically lacks a low-dimensional latent embedding but does not require training sequences to be aligned into an MSA and enable the design of variable length proteins. In this work, we propose ProtWave-VAE as a novel and lightweight DGM, employing an information maximizing VAE with a dilated convolution encoder and an autoregressive WaveNet decoder. This architecture blends the strengths of the VAE and AR paradigms in enabling training over unaligned sequence data and the conditional generative design of variable length sequences from an interpretable, low-dimensional learned latent space. We evaluated the model's ability to infer patterns and design rules within alignment-free homologous protein family sequences and to design novel synthetic proteins in four diverse protein families. We show that our model can infer meaningful functional and phylogenetic embeddings within latent spaces and make highly accurate predictions within semisupervised downstream fitness prediction tasks. In an application to the C-terminal SH3 domain in the Sho1 transmembrane osmosensing receptor in baker's yeast, we subject ProtWave-VAE-designed sequences to experimental gene synthesis and select-seq assays for the osmosensing function to show that the model enables synthetic protein design, conditional C-terminus diversification, and engineering of the osmosensing function into SH3 paralogues.

Phylogenetic Analysis of Acacia nilotica and Coffea arabica Using Protein Sequences from the Chloroplast RBCL Gene

The genus Acacia is important economically to local communities in sub-Saharan Africa for its medicinal and beverage usage. The bark extract is used for making a coffee-like concoction, which is named by locals as ‘Wild coffee’ due to its brown color. The objective of this study was to compare the evolutionary analysis of A. nilotica and C. arabica -based amino acids sequence of the ribulose-1,5-bisphosphate carboxylase. The results showed that A. nilotica and C. arabica are polyphyletic and the subspecies A. nilotica and A. n. hemispherica formed the sister group, same as the species C. arabica, C. salvatrix, and C. racemosa. The chloroplast-encoded rbcL gene, which encodes the large subunit of ribulose-1, 5-bisphosphate carboxylase/oxygenase (Rubisco), is a valuable marker for investigating the evolutionary relationships between plant species. In this study, we conducted a phylogenetic analysis of two economically and ecologically significant plants, Acacia nilotica and Coffea arabica, using protein sequences derived from the chloroplast rbcL gene. A multiple sequence alignment of the rbcL protein sequences was performed, and a maximum likelihood phylogenetic tree was constructed using the RAxML algorithm. The tree was rooted using a Thiotrichales bacterium as an outgroup sequence to establish the evolutionary context. Branch support values were calculated to assess the statistical robustness of the inferred relationships. The results of the phylogenetic analysis revealed the evolutionary relationship between Acacia nilotica and Coffea arabica within the context of other plant taxa. The phylogenetic tree provided insights into their shared ancestry, divergence time, and taxonomic placement within the larger plant kingdom. We identified conserved regions in the rbcL protein sequences, reflecting functional importance, as well as divergent regions, suggesting potential adaptive evolution. The significance of our study lies in understanding the evolutionary history and taxonomic position of these economically important plant species. This knowledge has implications for biodiversity conservation, crop improvement, and ecosystem management. The study also highlights the utility of the rbcL gene as a valuable tool for investigating plant phylogenetics. In conclusion, our phylogenetic analysis using the rbcL protein sequences provides valuable insights into the evolutionary relationship between Acacia nilotica and Coffea arabica. This research contributes to our understanding of plant evolution and has practical applications in various fields, from agriculture to conservation.

Genome-Wide Identification, Characterization and Expression Analysis of Plant Nuclear Factor (NF-Y) Gene Family Transcription Factors in Saccharum spp.

Plant nuclear factor (NF-Y) is a transcriptional activating factor composed of three subfamilies: NF-YA, NF-YB, and NF-YC. These transcriptional factors are reported to function as activators, suppressors, and regulators under different developmental and stress conditions in plants. However, there is a lack of systematic research on the NF-Y gene subfamily in sugarcane. In this study, 51 NF-Y genes (ShNF-Y), composed of 9 NF-YA, 18 NF-YB, and 24 NF-YC genes, were identified in sugarcane (Saccharum spp.). Chromosomal distribution analysis of ShNF-Ys in a Saccharum hybrid located the NF-Y genes on all 10 chromosomes. Multiple sequence alignment (MSA) of ShNF-Y proteins revealed conservation of core functional domains. Sixteen orthologous gene pairs were identified between sugarcane and sorghum. Phylogenetic analysis of NF-Y subunits of sugarcane, sorghum, and Arabidopsis showed that ShNF-YA subunits were equidistant while ShNF-YB and ShNF-YC subunits clustered distinctly, forming closely related and divergent groups. Expression profiling under drought treatment showed that NF-Y gene members were involved in drought tolerance in a Saccharum hybrid and its drought-tolerant wild relative, Erianthus arundinaceus. ShNF-YA5 and ShNF-YB2 genes had significantly higher expression in the root and leaf tissues of both plant species. Similarly, ShNF-YC9 had elevated expression in the leaf and root of E. arundinaceus and in the leaf of a Saccharum hybrid. These results provide valuable genetic resources for further sugarcane crop improvement programs.

Functional Protein Dynamics Directly from Sequences.

The sequence correlations within a protein multiple sequence alignment are routinely being used to predict contacts within its structure, but here we point out that these data can also be used to predict a protein's dynamics directly. The elastic network protein dynamics models rely directly upon the contacts, and the normal modes of motion are obtained from the decomposition of the inverse of the contact map. To make the direct connection between sequence and dynamics, it is necessary to apply coarse-graining to the structure at the level of one point per amino acid, which has often been done, and protein coarse-grained dynamics from elastic network models has been highly successful, particularly in representing the large-scale motions of proteins that usually relate closely to their functions. The interesting implication of this is that it is not necessary to know the structure itself to obtain its dynamics and instead to use the sequence information directly to obtain the dynamics.

PanPA: generation and alignment of panproteome graphs.

Compared to eukaryotes, prokaryote genomes are more diverse through different mechanisms, including a higher mutation rate and horizontal gene transfer. Therefore, using a linear representative reference can cause a reference bias. Graph-based pangenome methods have been developed to tackle this problem. However, comparisons in DNA space are still challenging due to this high diversity. In contrast, amino acid sequences have higher similarity due to evolutionary constraints, whereby a single amino acid may be encoded by several synonymous codons. Coding regions cover the majority of the genome in prokaryotes. Thus, panproteomes present an attractive alternative leveraging the higher sequence similarity while not losing much of the genome in non-coding regions. We present PanPA, a method that takes a set of multiple sequence alignments of protein sequences, indexes them, and builds a graph for each multiple sequence alignment. In the querying step, it can align DNA or amino acid sequences back to these graphs. We first showcase that PanPA generates correct alignments on a panproteome from 1350 Escherichia coli. To demonstrate that panproteomes allow comparisons at longer phylogenetic distances, we compare DNA and protein alignments from 1073 Salmonella enterica assemblies against E.coli reference genome, pangenome, and panproteome using BWA, GraphAligner, and PanPA, respectively; with PanPA aligning around 22% more sequences. We also aligned a DNA short-reads whole genome sequencing (WGS) sample from S.enterica against the E.coli reference with BWA and the panproteome with PanPA, where PanPA was able to find alignment for 68% of the reads compared to 5% with BWA. PanPA is available at https://github.com/fawaz-dabbaghieh/PanPA.

SHINE: protein language model-based pathogenicity prediction for short inframe insertion and deletion variants.

Accurate variant pathogenicity predictions are important in genetic studies of human diseases. Inframe insertion and deletion variants (indels) alter protein sequence and length, but not as deleterious as frameshift indels. Inframe indel Interpretation is challenging due to limitations in the available number of known pathogenic variants for training. Existing prediction methods largely use manually encoded features including conservation, protein structure and function, and allele frequency to infer variant pathogenicity. Recent advances in deep learning modeling of protein sequences and structures provide an opportunity to improve the representation of salient features based on large numbers of protein sequences. We developed a new pathogenicity predictor for SHort Inframe iNsertion and dEletion (SHINE). SHINE uses pretrained protein language models to construct a latent representation of an indel and its protein context from protein sequences and multiple protein sequence alignments, and feeds the latent representation into supervised machine learning models for pathogenicity prediction. We curated training data from ClinVar and gnomAD, and created two test datasets from different sources. SHINE achieved better prediction performance than existing methods for both deletion and insertion variants in these two test datasets. Our work suggests that unsupervised protein language models can provide valuable information about proteins, and new methods based on these models can improve variant interpretation in genetic analyses.

State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold.

The problem of predicting a protein's 3D structure from its primary amino acid sequence is a longstanding challenge in structural biology. Recently, approaches like alphafold have achieved remarkable performance on this task by combining deep learning techniques with coevolutionary data from multiple sequence alignments of related protein sequences. The use of coevolutionary information is critical to these models' accuracy, and without it their predictive performance drops considerably. In living cells, however, the 3D structure of a protein is fully determined by its primary sequence and the biophysical laws that cause it to fold into a low-energy configuration. Thus, it should be possible to predict a protein's structure from only its primary sequence by learning an approximate biophysical energy function. Weprovide evidence that alphafold has learned such an energy function, and uses coevolution data to solve the global search problem of finding a low-energy conformation. We demonstrate that alphafold'slearned energy function can be used to rank the quality of candidate protein structures with state-of-the-art accuracy, without using any coevolution data. Finally, we explore several applications of this energy function, including the prediction of protein structures without multiple sequence alignments.

Olfactory marker protein contains a leucine-rich domain in the Ω-loop important for nuclear export

Olfactory marker protein (OMP) is a cytosolic protein expressed in mature olfactory receptor neurons (ORNs). OMP modulates cAMP signalling and regulates olfactory sensation and axonal targeting. OMP is a small soluble protein, and passive diffusion between nucleus and cytoplasm is expected. However, OMP is mostly situated in the cytosol and is only sparsely detected in the nuclei of a subset of ORNs, hypothalamic neurons and heterologously OMP-expressing cultured cells. OMP can enter the nucleus in association with transcription factors. However, how OMP is retained in the cytosol at rest is unclear. Because OMP is proposed to affect cell differentiation, it is important to understand how OMP is distributed between cytoplasm and nucleus. To elucidate the structural profile of OMP, we applied several bioinformatics methods to a multiple sequence alignment (MSA) of OMP protein sequences and ranked the evolutionarily conserved residues. In addition to the previously reported cAMP-binding domain, we identified a leucine-rich domain in the Ω-loop of OMP. We introduced mutations into the leucine-rich region and heterologously expressed the mutant OMP in HEK293T cells. Mutations into alanine increased the nuclear distribution of OMP quantified by immunocytochemistry and western blotting. Therefore, we concluded that OMP contains a leucine-rich domain important for nuclear transport.

Protein structure–function exploration initiative in undergraduate biochemistry and independent research courses

Protein structure-function relationship serves as the primary learning outcome in any undergraduate biochemistry course. We expanded the protein structure-function exploration, PSFE initiative during COVID-19 to provide more effective and engaging experience to our undergraduates in biochemistry and independent research courses. Multiple alignments of protein sequences provided crucial insight into sequence conservation across many species and thus allow identification of those sections of the sequence most critical to protein function. We used Anabaena Sensory Rhodopsin, ASR its transducer, ASRT and downstream novel kinase gene products of Anabaena PCC 7120 to seek their alignment with homologs in available database. Pymol served an opportunity to achieve this goal (interactive learning during lab session and stimulation of course content discussion) in interesting ways. The PSFE initiative expansion continued during pandemic using online/hybrid modality. Initially model examples all helical ASR and beta-sheet ASRT were introduced to connect and integrate our ongoing research interest into classroom activities. Subsequently, undergraduates in biochemistry course were assigned a homolog of model proteins any particular protein of students choice to study and characterize using Pymol in semester. During first phase, each undergraduate worked independently using established guidelines. Student's exploration progress was periodically reviewed in pilot phase with majority of students who perceived it as challenging task successfully completed the assignment. Using the PyMol application to reinforce visual understanding of protein structure was highly satisfying experience that greatly enriched undergraduates understanding and appreciation. This article reports a session from the virtual international 2021 IUBMB/ASBMB workshop, "Teaching Science on BigData."

Analysis of lineage-specific protein family variability in prokaryotes combined with evolutionary reconstructions

BackgroundEvolutionary rate is a key characteristic of gene families that is linked to the functional importance of the respective genes as well as specific biological functions of the proteins they encode. Accurate estimation of evolutionary rates is a challenging task that requires precise phylogenetic analysis. Here we present an easy to estimate protein family level measure of sequence variability based on alignment column homogeneity in multiple alignments of protein sequences from Clade-Specific Clusters of Orthologous Genes (csCOGs).ResultsWe report genome-wide estimates of variability for 8 diverse groups of bacteria and archaea and investigate the connection between variability and various genomic and biological features. The variability estimates are based on homogeneity distributions across amino acid sequence alignments and can be obtained for multiple groups of genomes at minimal computational expense. About half of the variance in variability values can be explained by the analyzed features, with the greatest contribution coming from the extent of gene paralogy in the given csCOG. The correlation between variability and paralogy appears to originate, primarily, not from gene duplication, but from acquisition of distant paralogs and xenologs, introducing sequence variants that are more divergent than those that could have evolved in situ during the lifetime of the given group of organisms. Both high-variability and low-variability csCOGs were identified in all functional categories, but as expected, proteins encoded by integrated mobile elements as well as proteins involved in defense functions and cell motility are, on average, more variable than proteins with housekeeping functions. Additionally, using linear discriminant analysis, we found that variability and fraction of genomes carrying a given gene are the two variables that provide the best prediction of gene essentiality as compared to the results of transposon mutagenesis in Sulfolobus islandicus.ConclusionsVariability, a measure of sequence diversity within an alignment relative to the overall diversity within a group of organisms, offers a convenient proxy for evolutionary rate estimates and is informative with respect to prediction of functional properties of proteins. In particular, variability is a strong predictor of gene essentiality for the respective organisms and indicative of sub- or neofunctionalization of paralogs.

Parallel protein multiple sequence alignment approaches: a systematic literature review

Parallel protein multiple sequence alignment approaches: a systematic literature review

The CaALAD Gene From Pepper (Capsicum annuum L.) Confers Chilling Stress Tolerance in Transgenic Arabidopsis Plants.

The ALAD gene encodes an enzyme that is essential for chlorophyll biosynthesis and is involved in many other physiological processes in plants. In this study, the CaALAD gene was cloned from pepper and sequenced. Multiple sequence alignment and phylogenetic analysis of ALAD proteins from nine plant species showed that ALAD is highly conserved, and that CaALAD shows the highest homology with the ALAD protein from eggplant. Subcellular localization indicated that the CaALAD protein is mainly localized to the chloroplasts. After transferring CaALAD into the Arabidopsis thaliana genome, cold tolerance of the transgenic lines improved. Overexpression of CaALAD increased the relative transcription of the AtCBF2, AtICE1, and AtCOR15b genes in transgenic Arabidopsis plants exposed to low temperature (4°C) stress, and the contents of reactive oxygen species decreased due to increased activities of superoxide dismutase, peroxidase, and catalase. Moreover, chlorophyll biosynthesis, as determined by the contents of porphobilinogen, protoporphyrin IX, Mg-protoporphyrin IX, prochlorophyllate, and chlorophyll in the transgenic Arabidopsis plants, increased in response to low temperature stress. In addition, the transgenic lines were more sensitive to exogenous ALA and NaHS, and the H2S content of transgenic line plants increased more rapidly than in the wild-type, suggesting that CaALAD may respond to low temperatures by influencing the content of H2S, a signaling molecule. Our study gives a preliminary indication of the function of CaALAD and will provide a theoretical basis for future molecular breeding of cold tolerance in pepper.