Protein Database Research Articles

The three-sided right-handed β-helix is a versatile fold for glycan interactions.

Interactions between proteins and glycans are critical to various biological processes. With databases of carbohydrate-interacting proteins and increasing amounts of structural data, the three-sided right-handed β-helix (RHBH) has emerged as a significant structural fold for glycan interactions. In this review, we provide an overview of the sequence, mechanistic, and structural features that enable the RHBH to interact with glycans. The RHBH is a prevalent fold that exists in eukaryotes, prokaryotes, and viruses associated with adhesin and carbohydrate-active enzyme (CAZyme) functions. An evolutionary trajectory analysis on structurally characterized RHBH-containing proteins shows that they likely evolved from carbohydrate-binding proteins with their carbohydrate-degrading activities evolving later. By examining three polysaccharide lyase and three glycoside hydrolase structures, we provide a detailed view of the modes of glycan binding in RHBH proteins. The 3-dimensional shape of the RHBH creates an electrostatically and spatially favorable glycan binding surface that allows for extensive hydrogen bonding interactions, leading to favorable and stable glycan binding. The RHBH is observed to be an adaptable domain capable of being modified with loop insertions and charge inversions to accommodate heterogeneous and flexible glycans and diverse reaction mechanisms. Understanding this prevalent protein fold can advance our knowledge of glycan binding in biological systems and help guide the efficient design and utilization of RHBH-containing proteins in glycobiology research.

A novel cathelicidin TS-CATH derived from Thamnophis sirtalis combats drug-resistant gram-negative bacteria in vitro and in vivo

Antimicrobial peptides are promising therapeutic agents for treating drug-resistant bacterial disease due to their broad-spectrum antimicrobial activity and decreased susceptibility to evolutionary resistance. In this study, three novel cathelicidin antimicrobial peptides were identified from Thamnophis sirtalis, Balaenoptera musculus, and Lipotes vexillifer by protein database mining and sequence alignment and were subsequently named TS-CATH, BM-CATH, and LV-CATH, respectively. All three peptides exhibited satisfactory antibacterial activity and broad antibacterial spectra against clinically isolated E. coli, P. aeruginosa, K. pneumoniae, and A. baumannii in vitro. Among them, TS-CATH displayed the best antimicrobial/bactericidal activity, with a rapid elimination efficiency against the tested drug-resistant gram-negative bacteria within 20 min, and exhibited the lowest cytotoxicity toward mammalian cells. Furthermore, TS-CATH effectively enhanced the survival rate of mice with ceftazidime-resistant E. coli bacteremia and promoted wound healing in meropenem-resistant P. aeruginosa infection. These results were achieved through the eradication of bacterial growth in target organs and wounds, further inhibiting the systemic dissemination of bacteria and the inflammatory response. TS-CATH exhibited direct antimicrobial activity by damaging the inner and outer membranes, resulting in leakage of the bacterial contents at super-MICs. Moreover, TS-CATH disrupted the bacterial respiratory chain, which inhibited ATP synthesis and induced ROS formation, significantly contributing to its antibacterial efficacy at sub-MICs. Overall, TS-CATH has potential for use as an antibacterial agent.

Bioactive peptides derived from the enzymatic hydrolysis of cowhide collagen for the potential treatment of atherosclerosis: A computational approach

Cowhide collagen hydrolysates (CCHs) are peptides and amino acids obtained from the partial hydrolysis of collagen. These have numerous potential applications in the food, biomedical, and pharmaceutical industries. The study analyzed the physicochemical, antioxidant, and anti-atherosclerosis properties of collagen hydrolysates (CCHs) from cowhide using in silico methods. Proteins were identified in silico based on their molecular weights and origin from the protein database (UniProtKB). Using bioinformatics tools, numerous physicochemical properties (toxicity and amino acid composition) were determined. The identified proteins were subsequently subjected to an in silico enzymatic hydrolysis using pepsin, thermolysin, and proteinase K. The peptides obtained were characterized. Molecular docking was conducted between the peptides generated in silico and the three target enzymes (3-Hydroxy-3-Methylglutaryl-CoA (HMG-CoA) reductase, cyclooxygenase-2, and Nicotinamide Adenine Dinucleotide Phosphate (NADPH) oxidase). Two cowhide collagens were identified, F1MJQ6 and G3MZI7, with molecular weights of 172,076 and 184,867 ​Da, respectively. A compositional analysis of F1MJQ6 and G3MZI7 revealed the significant presence of glycine residues at 25% and 23%, and proline residues at 16% and 18%, respectively. The G3MZI7 and F1MJQ6 proteins exhibited a high concentration of both essential and semi-essential amino acids. The molecular docking results indicate that the antioxidant peptides ADF, PHF, and LW (novel potential anti-atherosclerosis peptides released by enzymatic hydrolysis with pepsin, thermolysin, and proteinase K) are the most promising candidates for further development as inhibitors of HMG-CoA reductase, cyclo-oxygenase-2, and NADPH oxidase. In silico analysis revealed that cowhide collagen hydrolysates exhibited particularly significant antioxidant and anti-atherosclerosis properties.

Genetic and functional diversity of β-N-acetylgalactosamine-targeting glycosidases expanded by deep-sea metagenome analysis

β-N-Acetylgalactosamine-containing glycans play essential roles in several biological processes, including cell adhesion, signal transduction, and immune responses. β-N-Acetylgalactosaminidases hydrolyze β-N-acetylgalactosamine linkages of various glycoconjugates. However, their biological significance remains ambiguous, primarily because only one type of enzyme, exo-β-N-acetylgalactosaminidases that specifically act on β-N-acetylgalactosamine residues, has been documented to date. In this study, we identify four groups distributed among all three domains of life and characterize eight β-N-acetylgalactosaminidases and β-N-acetylhexosaminidase through sequence-based screening of deep-sea metagenomes and subsequent searching of public protein databases. Despite low sequence similarity, the crystal structures of these enzymes demonstrate that all enzymes share a prototype structure and have diversified their substrate specificities (oligosaccharide-releasing, oligosaccharide/monosaccharide-releasing, and monosaccharide-releasing) through the accumulation of mutations and insertional amino acid sequences. The diverse β-N-acetylgalactosaminidases reported in this study could facilitate the comprehension of their structures and functions and present evolutionary pathways for expanding their substrate specificity.

Analysis of the presence of anti-viral innate immune pathways in the Australian Haliotis laevigata

Our understanding of the specificities of innate immune protection against viral pathogens in non-model invertebrate species is limited, and comparative immunological approaches may offer opportunities to protect aquaculturally significant species such as abalone, against viral threats. Haliotid herpesvirus (HaHV-1) is a re-emerging viral pathogen of abalone, and has a high mortality rate in the dominant wild and farmed species in Australia, the Australian greenlip abalone (Haliotis laevigata), the blacklip (Haliotis rubra) and a hybrid of the two. To gain a better understanding of the anti-viral immune system counterparts of Australian abalone, gene mining strategies and domain analysis of the recently released H. laevigata draft genome was performed, with comparisons made to the better studied mollusc, the oyster (Crassostrea gigas). Significant divergence in members of the TLR, cytosolic RNA/DNA and RNA interference signalling pathways was observed between the two molluscs, with the oyster more closely reflecting that of the mammalian immune response. Interestingly, a homolog for the anti-viral RNA sensing adaptor protein Mitochondrial anti-viral-signalling protein was absent in H. laevigata, as well as the 4 other abalone protein databases currently available, despite the presence of a RIG-I homolog. Additionally, STING, a member of the dsDNA sensing pathway was absent in H. laevigata, as well as 2 other abalone species, however is present in the oyster genome, and two additional abalone species (Haliotis rufescens, Haliotis discus hannai).This work provides a better understanding of the key features of the abalone anti-viral innate immune signalling system and may provide much needed information towards the development of anti-viral strategies in these animals.

BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA.

Gene prediction has remained an active area of bioinformatics research for a long time. Still, gene prediction in large eukaryotic genomes presents a challenge that must be addressed by new algorithms. The amount and significance of the evidence available from transcriptomes and proteomes vary across genomes, between genes, and even along a single gene. User-friendly and accurate annotation pipelines that can cope with such data heterogeneity are needed. The previously developed annotation pipelines BRAKER1 and BRAKER2 use RNA-seq or protein data, respectively, but not both. A further significant performance improvement integrating all three data types was made by the recently released GeneMark-ETP. We here present the BRAKER3 pipeline that builds on GeneMark-ETP and AUGUSTUS, and further improves accuracy using the TSEBRA combiner. BRAKER3 annotates protein-coding genes in eukaryotic genomes using both short-read RNA-seq and a large protein database, along with statistical models learned iteratively and specifically for the target genome. We benchmarked the new pipeline on genomes of 11 species under an assumed level of relatedness of the target species proteome to available proteomes. BRAKER3 outperforms BRAKER1 and BRAKER2. The average transcript-level F1-score is increased by about 20 percentage points on average, whereas the difference is most pronounced for species with large and complex genomes. BRAKER3 also outperforms other existing tools, MAKER2, Funannotate, and FINDER. The code of BRAKER3 is available on GitHub and as a ready-to-run Docker container for execution with Docker or Singularity. Overall, BRAKER3 is an accurate, easy-to-use tool for eukaryotic genome annotation.

Improvements in viral gene annotation using large language models and soft alignments

BackgroundThe annotation of protein sequences in public databases has long posed a challenge in molecular biology. This issue is particularly acute for viral proteins, which demonstrate limited homology to known proteins when using alignment, k-mer, or profile-based homology search approaches. A novel methodology employing Large Language Models (LLMs) addresses this methodological challenge by annotating protein sequences based on embeddings.ResultsCentral to our contribution is the soft alignment algorithm, drawing from traditional protein alignment but leveraging embedding similarity at the amino acid level to bypass the need for conventional scoring matrices. This method not only surpasses pooled embedding-based models in efficiency but also in interpretability, enabling users to easily trace homologous amino acids and delve deeper into the alignments. Far from being a black box, our approach provides transparent, BLAST-like alignment visualizations, combining traditional biological research with AI advancements to elevate protein annotation through embedding-based analysis while ensuring interpretability. Tests using the Virus Orthologous Groups and ViralZone protein databases indicated that the novel soft alignment approach recognized and annotated sequences that both blastp and pooling-based methods, which are commonly used for sequence annotation, failed to detect.ConclusionThe embeddings approach shows the great potential of LLMs for enhancing protein sequence annotation, especially in viral genomics. These findings present a promising avenue for more efficient and accurate protein function inference in molecular biology.

Proteomic Profile of Flaxseed (Linum usitatissimum L.) Products as Influenced by Protein Concentration Method and Cultivar.

The research is focused on the quantitative evaluation of the flaxseed (Linum usitatissimum L.) proteome at the level of seed cake (SC), fine flour-sieved a fraction below 250 µm (FF)-and protein concentrate (PC). The evaluation was performed on three oilseed flax cultivars (Agriol, Raciol, and Libra) with different levels of α-linolenic acid content using LC-MS/MS (shotgun proteomics) analysis, which was finalized by database searching using the NCBI protein database for Linum usitatissimum and related species. A total of 2560 protein groups (PGs) were identified, and their relative abundance was calculated. A set of 33 quantitatively most significant PGs was selected for further characterization. The selected PGs were divided into four classes-seed storage proteins (11S globulins and conlinins), oleosins, defense- and stress-related proteins, and other major proteins (mainly including enzymes). Seed storage proteins were found to be the most abundant proteins. Specifically, 11S globulins accounted for 41-44% of SC proteins, 40-46% of FF proteins, and 72-84% of PC proteins, depending on the cultivar. Conlinins (2S albumins) were the most abundant in FF, ranging from 10 to 13% (depending on cultivar). The second most important class from the point of relative abundance was oleosins, which were represented in SC and FF in the range of 2.1-3.8%, but only 0.36-1.20% in PC. Surprisingly, a relatively high abundance of chitinase was found in flax products as a protein related to defence and stress reactions.

Database selection for shotgun metaproteomic of low-diversity dairy microbiomes

The metaproteomics field has recently gained more and more interest as a valuable tool for studying both the taxonomy and function of microbiomes, including those used in food fermentations. One crucial step in the metaproteomics pipeline is selecting a database to obtain high-quality taxonomical and functional information from microbial communities. One of the best strategies described for building protein databases is using sample-specific or study-specific protein databases obtained from metagenomic sequencing. While this is true for high-diversity microbiomes (such as gut and soil), there is still a lack of validation for different database construction strategies in low-diversity microbiomes, such as those found in fermented dairy products where starter cultures containing few species are used. In this study, we assessed the performance of various database construction strategies applied to metaproteomics on two low-diversity microbiomes obtained from cheese production using commercial starter cultures and analyzed by LC-MS/MS. Substantial differences were detected between the strategies, and the best performance in terms of the number of peptides and proteins identified from the spectra was achieved by metagenomic-derived databases. However, extensive databases constructed from a high number of available online genomes obtained a similar taxonomical and functional annotation of the metaproteome compared to the metagenomic-derived databases. Our results indicate that, in the case of low-diversity dairy microbiomes, the use of publically available genomes to construct protein databases can be considered as an alternative to metagenome-derived databases.

Graphical models for identifying pore-forming proteins.

Pore-forming toxins (PFTs) are proteins that form lesions in biological membranes. Better understanding of the structure and function of these proteins will be beneficial in a number of biotechnological applications, including the development of new pest control methods in agriculture. When searching for new pore formers, existing sequence homology-based methods fail to discover truly novel proteins with low sequence identity to known proteins. Search methodologies based on protein structures would help us move beyond this limitation. As the number of known structures for PFTs is very limited, it's quite challenging to identify new proteins having similar structures using computational approaches like deep learning. In this article, we therefore propose a sample-efficient graphical model, where a protein structure graph is first constructed according to consensus secondary structures. A semi-Markov conditional random fields model is then developed to perform protein sequence segmentation. We demonstrate that our method is able to distinguish structurally similar proteins even in the absence of sequence similarity (pairwise sequence identity < 0.4)-a feat not achievable by traditional approaches like HMMs. To extract proteins of interest from a genome-wide protein database for further study, we also develop an efficient framework for UniRef50 with 43 million proteins.

DhuFAP: a platform for gene functional analysis in Dendrobium huoshanense

BackgroundDendrobium huoshanense, a traditional medicinal and food plant, has a rich history of use. Recently, its genome was decoded, offering valuable insights into gene function. However, there is no comprehensive gene functional analysis platform for D. huoshanense.ResultTo address this, we created a platform for gene function analysis and comparison in D. huoshanense (DhuFAP). Using 69 RNA-seq samples, we constructed a gene co-expression network and annotated D. huoshanense genes by aligning sequences with public protein databases. Our platform contained tools like Blast, gene set enrichment analysis, heatmap analysis, sequence extraction, and JBrowse. Analysis revealed co-expression of transcription factors (C2H2, GRAS, NAC) with genes encoding key enzymes in alkaloid biosynthesis. We also showcased the reliability and applicability of our platform using Chalcone synthases (CHS).ConclusionDhuFAP (www.gzybioinformatics.cn/DhuFAP) and its suite of tools represent an accessible and invaluable resource for researchers, enabling the exploration of functional information pertaining to D. huoshanense genes. This platform stands poised to facilitate significant biological discoveries in this domain.

IN SILICO SCREENING BY MOLECULAR DOCKING OF HETEROCYCLIC COMPOUNDS WITH FURAN OR INDOLE NUCLEUS FROM DATABASE FOR ANTICANCER ACTIVITY AND VALIDATION OF THE METHOD BY REDOCKING

Objective: This study aims to perform in silico screening of nine heterocyclic ligands containing furan or indole with oxygen in their structure selected from the compound database based on a literature review for predicting their anticancer activity on tyrosine kinase receptor receptors. Methods: The receptor is complex with the ligand Gliteritinib and was downloaded from the protein database. The ligands used for this study were 5-fluoro-1H-indole-2-carboxylic acid,2(5H)-Furanone Furfuryl pentanoate, Furan-2,5-dicarbaldehyde, 2,5-Furandicarboxylic acid, Furan-2-yl(1H-indol-3-yl) methanone, Tert-butyl 3-formyl-1H-indole-1-carboxylate,7-Amino-5-fluoroindolin-2-one,7H-Furo[3,2-g]chromen-7-one. Pyrex molecular docking software was used to perform the analysis. The study was validated using a re-docking technique using the ligand Gliteritinib. Results: A good docking score of (-7.8) was obtained for tert-butyl 3-formyl-1H-indole-1-carboxylate, leading to promising activity prediction. Furan-2-yl(1H-indol-3-yl) methadone and 7H-Furo[3,2-g]chromen-7-one also scored well with (-7.5) and (-7.3) respectively. The redocking process resulted in a score of (-9.2). Conclusion: Values are comparable to the root primary square value, showing the reproducibility of this method. The finding gives insight into Insilco docking for anticancer activity and further exploration of phytochemicals for Insilco screening.

Venom diversity in Naja mossambica: Insights from proteomic and immunochemical analyses reveal intraspecific differences.

Intraspecific variations in snake venom composition have been extensively documented, contributing to the diverse clinical effects observed in envenomed patients. Understanding these variations is essential for developing effective snakebite management strategies and targeted antivenom therapies. We aimed to comprehensively investigate venoms from three distinct populations of N. mossambica from Eswatini, Limpopo, and KwaZulu-Natal regions in Africa in terms of their protein composition and reactivity with three commercial antivenoms (SAIMR polyvalent, EchiTAb+ICP, and Antivipmyn Africa). Naja mossambica venoms from Eswatini region exhibited the highest content of neurotoxic proteins, constituting 20.70% of all venom proteins, compared to Limpopo (13.91%) and KwaZulu-Natal (12.80%), and was characterized by the highest diversity of neurotoxic proteins, including neurotoxic 3FTxs, Kunitz-type inhibitors, vespryns, and mamba intestinal toxin 1. KwaZulu-Natal population exhibited considerably lower cytotoxic 3FTx, higher PLA2 content, and significant diversity in low-abundant proteins. Conversely, Limpopo venoms demonstrated the least diversity as demonstrated by electrophoretic and mass spectrometry analyses. Immunochemical assessments unveiled differences in venom-antivenom reactivity, particularly concerning low-abundance proteins. EchiTAb+ICP antivenom demonstrated superior reactivity in serial dilution ELISA assays compared to SAIMR polyvalent. Our findings reveal a substantial presence of neurotoxic proteins in N. mossambica venoms, challenging previous understandings of their composition. Additionally, the detection of numerous peptides aligning to uncharacterized proteins or proteins with unknown functions underscores a critical issue with existing venom protein databases, emphasizing the substantial gaps in our knowledge of snake venom protein components. This underscores the need for enhanced research in this domain. Moreover, our in vitro immunological assays suggest EchiTAb+ICP's potential as an alternative to SAIMR antivenom, requiring confirmation through prospective in vivo neutralization studies.

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.

EXPLORING THE ANTI-CANCER POTENTIAL OF PHYTOCHEMICALS FROM SPECIFIC PLANTS: EXAMINING AND VALIDATING THROUGH MOLECULAR DOCKING AND MD SIMULATIONS

Worldwide, cancer is the leading cause of death. Anti-cancer medications frequently induce side effects and multidrug resistance (MDR), which continues to be a key obstacle to effective cancer therapy. Essential nutrients and functionally bioactive substances can both be found in abundance in plants. The phytochemical components have great promise for treating both plant and human ailments. This study is designed to conduct an in-silico analysis on phytochemicals derived from Combretaceae family plants for targeting the proteins 4UWH, 5LWM, and 6P3D. The Combretaceae family has demonstrated pharmacological benefits such as anti-leishmanial, cytotoxic, antibacterial, antidiabetic, antiprotozoal, anticancer, and antifungal qualities. To conduct experiments with the natural phytochemicals against the proteins, computerized tools, online servers, and online databases were used. 196 natural compounds were used for virtual screening out of the top 5 best-docked compounds selected based on their binding energy. The best-selected phytochemicals possessed potential results in 10ns molecular dynamic simulation. So, it is convincible based on in-silico research this selected phytochemical has the potential to serve as a promising lead compound against cancer.

Comparative Genomic Analysis of the Mutant Rhodotorula mucilaginosa JH-R23 Provides Insight into the High-Yield Carotenoid Mechanism

In this study, the wild-type Rhodotorula mucilaginosa GDMCC 2.30 and its high carotenoid-producing mutant JH-R23, which was screened from the space mutation breeding treated wild type, were used as materials. Through whole-genome sequencing and resequencing analysis, the carotenoid metabolic pathway and mechanism of high carotenoid production in the mutant were explored. The R. mucilaginosa GDMCC 2.30 genome comprised 18 scaffolds and one circular mitochondrial genome with a total size of 20.31 Mb, a GC content of 60.52%, and encoding 7128 genes. The mitochondrial genome comprised 40,152 bp with a GC content of 40.59%. Based on functional annotations in the GO, KEGG, and other protein databases, nine candidate genes associated with carotenoid metabolic pathways, and candidate genes of the CrtS and CrtR homologous gene families were identified. The carotenoid metabolic pathway was inferred to start from sugar metabolism to the mevalonate pathway, as is common to most fungi, and the final product of the mevalonate pathway, geranylgeranyl diphosphate, is a precursor for various carotenoids, including β-carotene, lycopene, astaxanthin, and torularhodin, formed through the activity of crucial enzymes encoded by genes such as CrtI, CrtYB, CrtS, and CrtR. Resequencing analysis of the mutant JH-R23 detected mutations in the exons of four genes, including those encoding Gal83, 3-oxoacyl-reductase, p24 proteins, and GTPase. These mutations are interpreted to have an important impact on carotenoid synthesis by JH-R23.

Abstract 788: Leveraging multi omics approach to examine the potential role of causative viruses in glioblastoma

Abstract Glioblastoma (GB), a grade IV brain cancer, has the lowest survival rate. Despite aggressive and targeted therapies, patients remain to have the worst prognosis. More than 16% of cancer incidence worldwide has been attributed to infectious agents, raising uncertainties about the role of infections in the development of brain cancer. While numerous studies have suggested potential viral involvement in GB pathogenesis, a common limitation is the reliance on a singular technology, such as DNA/RNA sequencing or immunohistochemistry, within each study. This approach may restrict the comprehensive exploration of viral contributions, as different technologies offer distinct insights into the complex mechanisms underlying GB development. Utilizing a combination of techniques could provide a more holistic understanding, enhancing the robustness of findings and potentially uncovering nuanced aspects of viral influence that a single technology might overlook. To clarify the role of viruses in GB pathology, our study employs a comprehensive approach integrating three distinct research methods. We screened 178 publicly accessible raw mass spectrometry (MS) data of GB tumors from three independent publications (PMID: 31331834, 31154438, 36720864) by employing our internally developed data identification pipeline. The pipeline transforms raw MS data for downstream analysis, conducts database searches against the latest viral protein databases and undergoes peptide validation for enhanced robustness. Subsequent analysis filters duplicate hits and consolidates nested peptides. The final protein list includes only those identified with two or more peptides of over eight amino acids. The pipeline concludes by inferring and listing the corresponding viruses, ensuring a comprehensive and refined approach to the identification and analysis of viral proteins in the context of MS data. To enhance this dataset, we incorporated whole-genome metagenomics and MS-based proteomics, examining a preliminary cohort of15 GB tumor tissues. This approach explored a wide range of viral encoded proteins. Our comprehensive analysis, integrating bioinformatics, genomics, and proteomics, identified multiple herpesvirus species across our cohorts. This potential identification of viruses within GB has the capacity to redefine the stratification of tumor types in GB patients, signaling a significant development in our understanding of the disease. Citation Format: Bavani Gunasegaran, Aziz Abdullah A Alnakli, Gilles J. Guillemin4, Seong Beom Ahn, Benjamin Heng. Leveraging multi omics approach to examine the potential role of causative viruses in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 788.

In silico approaches for the identification of potential allergens among hypothetical proteins from Alternaria alternata and its functional annotation

Direct exposure to the fungal species Alternaria alternata is a major risk factor for the development of asthma, allergic rhinitis, and inflammation. As of November 23rd 2020, the NCBI protein database showed 11,227 proteins from A. alternata genome as hypothetical proteins (HPs). Allergens are the main causative of several life-threatening diseases, especially in fungal infections. Therefore, the main aim of the study is to identify the potentially allergenic inducible proteins from the HPs in A. alternata and their associated functional assignment for the complete understanding of the complex biological systems at the molecular level. AlgPred and Structural Database of Allergenic Proteins (SDAP) were used for the prediction of potential allergens from the HPs of A. alternata. While analyzing the proteome data, 29 potential allergens were predicted by AlgPred and further screening in SDAP confirmed the allergic response of 10 proteins. Extensive bioinformatics tools including protein family classification, sequence-function relationship, protein motif discovery, pathway interactions, and intrinsic features from the amino acid sequence were used to successfully predict the probable functions of the 10 HPs. The functions of the HPs are characterized as chitin-binding, ribosomal protein P1, thaumatin, glycosyl hydrolase, and NOB1 proteins. The subcellular localization and signal peptide prediction of these 10 proteins has further provided additional information on localization and function. The allergens prediction and functional annotation of the 10 proteins may facilitate a better understanding of the allergenic mechanism of A. alternata in asthma and other diseases. The functional domain level insights and predicted structural features of the allergenic proteins help to understand the pathogenesis and host immune tolerance. The outcomes of the study would aid in the development of specific drugs to combat A. alternata infections.

Fast peak error correction algorithms for proteoform identification using top-down tandem mass spectra.

Proteoform identification is an important problem in proteomics. The main task is to find a modified protein that best fits the input spectrum. To overcome the combinatorial explosion of possible proteoforms, the proteoform mass graph and spectrum mass graph are used to represent the protein database and the spectrum, respectively. The problem becomes finding an optimal alignment between the proteoform mass graph and the spectrum mass graph. Peak error correction is an important issue for computing an optimal alignment between the two input mass graphs. We propose a faster algorithm for the error correction alignment of spectrum mass graph and proteoform mass graph problem and produce a program package TopMGFast. The newly designed algorithms require less space and running time so that we are able to compute global optimal alignments for the two input mass graphs in a reasonable time. For the local alignment version, experiments show that the running time of the new algorithm is reduced by 2.5 times. For the global alignment version, experiments show that the maximum mass errors between any pair of matched nodes in the alignments obtained by our method are within a small range as designed, while the alignments produced by the state-of-the-art method, TopMG, have very large maximum mass errors for many cases. The obtained alignment sizes are roughly the same for both TopMG and TopMGFast. Of course, TopMGFast needs more running time than TopMG. Therefore, our new algorithm can obtain more reliable global alignments within a reasonable time. This is the first time that global optimal error correction alignments can be obtained using real datasets. The source code of the algorithm is available at https://github.com/Zeirdo/TopMGFast.

VirtuousPocketome: a computational tool for screening protein–ligand complexes to identify similar binding sites

Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein–ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein–ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein–ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein–ligand complex, and could be expanded to screen any database of protein structures.