Similarity Search Tools Research Articles

Evaluating the Physicochemical Properties-Activity Relationship and Discovering New 1,2-Dihydropyridine Derivatives as Promising Inhibitors for PIM1-Kinase: Evidence from Principal Component Analysis, Molecular Docking, and Molecular Dynamics Studies.

In this study, we evaluated the physicochemical properties related to the previously reported anticancer activity of a dataset comprising thirty 1,2-dihydropyridine derivatives. We utilized Principal Component Analysis (PCA) to identify the most significant influencing factors. The PCA analysis showed that the first two principal components accounted for 59.91% of the total variance, indicating a strong correlation between the molecules and specific descriptors. Among the 239 descriptors analyzed, 18 were positively correlated with anticancer activity, clustering with the 12 most active compounds based on their IC50 values. Six of these variables-LogP, Csp3, b_1rotN, LogS, TPSA, and lip_don-are related to drug-likeness potential. Thus, we then ranked the 12 compounds according to these six variables and excluded those violating the drug-likeness criteria, resulting in a shortlist of nine compounds. Next, we investigated the binding affinity of these nine shortlisted compounds with the use of molecular docking towards the PIM-1 Kinase enzyme (PDB: 2OBJ), which is overexpressed in various cancer cells. Compound 6 exhibited the best docking score among the docked compounds, with a docking score of -11.77 kcal/mol, compared to -12.08 kcal/mol for the reference PIM-1 kinase inhibitor, 6-(5-bromo-2-hydroxyphenyl)-2-oxo-4-phenyl-1,2-dihydropyridine-3-carbonitrile. To discover new PIM-1 kinase inhibitors, we designed nine novel compounds featuring hybrid structures of compound 6 and the reference inhibitor. Among these, compound 31 displayed the best binding affinity, with a docking score of -13.11 kcal/mol. Additionally, we performed PubChem database mining using the structure of compound 6 and the similarity search tool, identifying 16 structurally related compounds with various reported biological properties. Among these, compound 52 exhibited the best binding affinity, with a docking score of -13.03 kcal/mol. Finally, molecular dynamics (MD) studies were conducted to confirm the stability of the protein-ligand complexes obtained from docking the studied compounds to PIM-1 kinase, validating the potential of these compounds as PIM-1 kinase inhibitors.

SiteMine: Large-scale binding site similarity searching in protein structure databases.

Drug discovery and design challenges, such as drug repurposing, analyzing protein-ligand and protein-protein complexes, ligand promiscuity studies, or function prediction, can be addressed by protein binding site similarity analysis. Although numerous tools exist, they all have individual strengths and drawbacks with regard to run time, provision of structure superpositions, and applicability to diverse application domains. Here, we introduce SiteMine, an all-in-one database-driven, alignment-providing binding site similarity search tool to tackle the most pressing challenges of binding site comparison. The performance of SiteMine is evaluated on the ProSPECCTs benchmark, showing a promising performance on most of the data sets. The method performs convincingly regarding all quality criteria for reliable binding site comparison, offering a novel state-of-the-art approach for structure-based molecular design based on binding site comparisons. In a SiteMine showcase, we discuss the high structural similarity between cathepsin L and calpain 1 binding sites and give an outlook on the impact of this finding on structure-based drug design. SiteMine is available at https://uhh.de/naomi.

Prediction of Antigenic Vaccine Peptide Candidates From BfmRS Associated With Biofilm Formation in Acinetobacter baumannii.

A. baumannii is categorized as a priority pathogen due to its propensity for multi-drug resistance, exhibiting resistance against the last resort of antibiotics. It is also considered a potent nosocomial pathogen, so targeting the microbe using novel strategies would be the need of the hour. In this context, the in-silico computational approach would serve the best to design the possible epitope peptides, which may be further considered for the experimental trials for their immunological response. Objective: To predict the immune-dominant epitope peptide candidates against the bfmRandbfmS proteins mediating the two-component system adaptation in the formation of biofilm in A. baumannii. 11 different FASTA sequences ofbfmRandbfmSfromA. baumannii strains retrieved based on the blast-p similarity search tool were subjected to linear epitope B-cell epitope predictions under the IEDB B-cell epitope prediction server. Further analysis on antigenicity, allergenicity, and toxigenicity was achieved using the AntigenPro, Vaxijen, and AlgPred tools, with the physical and chemical properties evaluated using the Expasy Protparam server. Selection of the immunodominant peptides for T-cells was done through the databases under IEDB. The final assessment of protein-TLR2 interactions was done by MHC cluster servers. Four peptide sequences (E1-E4) were predicted for B-cell dominance, with E1, E2, and E4 as probable antigens. All were soluble and non-toxigenic. E1 and E3 were considered non-allergens. GRAVY values were negative for all the peptides, indicating the protein to be hydrophilic in nature. Analysis of the T-cell epitopes was promising, with 100% conservancy for class-I HLA alleles, high interaction scores for similarity with TLR2, and more hydrogen bonds for E2, followed by other epitope peptides. The promising four epitopes, as predicted for bfmRandbfmS in the present study, suggest their potent role as possible candidates for the design of vaccines targeting the TCSofA. baumannii, recommending further in vitroand in vivoexperimental validation.

Identifying Similar Allergens and Potentially Cross-Reacting Areas Using Structural Database of Allergenic Proteins (SDAP) Tools and D-Graph.

The Structural Database of Allergenic Proteins (SDAP) provides rapid search tools to identify similarities among allergens, their IgE epitopes, and to determine the potential allergenicity of any novel protein. Many labs have identified IgE-binding proteins and their antibody binding or T cell epitopes using dotspots or microarrays. This chapter describes how to determine the relationship of these proteins and peptides to known allergens using the tools implemented in SDAP. One can also search with these smaller peptide similarity search tool implemented in SDAP to find similar sequences with low property distance (PD) values in the over 1500 sequences of allergens. The sequences can be compared by mapping on the surface of the protein structures provided for nearly all the allergens in SDAP. Once the user has a unique list of similar sequences, they can be graphed in 2D according to interpeptide PD values calculated automatically by the D-graph program. This chapter provides a step-by-step description of how to do this, starting from a protein similar to the Ole e 1 (olive pollen) allergen family.

Isolation, characterization and identification of cellulase (Endo-β-1,4-glucanase) producing bacteria from diverse locations


 The objective of the study was to isolate cellulase producing bacteria from natural sources and identify the bacterial isolates that produce thermostable enzyme. Bacteria were isolated from samples collected from different locations like mango leaf litter, termite gut, composting yard (municipal solid waste, compost, and soil), soil from decaying paper waste, decaying wood, and saline coastal belt soil by enrichment in broth containing 1% cellulose and subsequent culturing on plates and the purified cultures stored in slants of the solidified agar medium of the same composition as enrichment medium. Primary screening of the isolates for cellulase (Endo β-1,4-glucanase) production was done by growing them on Carboxy Methyl Cellulose (CMC) agar medium and screening using indicator dye Congo red. Secondary screening of the positive isolates showing cellulolytic Index > 4 was done by crude cellulase enzyme extraction and assay using 1% CMC in 1N citrate buffer (pH 5.0) at different pH values (4.8- 7.0) and temperatures (35- 70℃). Cellulase activity (EA) in U/mL was calculated based on the absorbance change per min. which depends on the amount of reducing sugar produced by the enzyme catalysed conversion of CMC to reducing sugar. Isolates were characterized morphologically and biochemically and six isolates (Gram -ve: T1, MSW, C1, S2 and Gram +ve: W2, P) having significantly higher EA were selected for DNA extraction. DNA was quantified and the DNA of four isolates (MSW, S2, W2, P) were selected for sequence analysis. The 16S rDNA sequencing was carried out through PCR amplification and Blast using NCBI blast similarity search tool resulted in the identification of MSW, S2, W2 as E.coli, Sulfitobacter pontiacus, Bacillus subtilis respectively and P as Bacillus cereus and Rhodococcus erythropolis strain e1. Isolates showed significantly higher cellulase activity (EA) at pH 6.2 and at varying temperatures. Bacterial isolates P and C2 showed significantly higher activity at 40℃, MSW at 60℃ and others at 50℃ (EA ranging from 0.0054 – 0.0604 U/mL). Isolates W2, P, S2 had EA ranging from 0.006 - 0.03 U/mL at 70℃. This study showed that the cellulase producers could be grown at different temperatures (40 - 70℃) on cellulose based substrate. In this study the substrate specificity indicates that the crude enzyme is an endo- β-1,4-glucanase that plays a prominent role in initiating and sustaining the hydrolytic process and randomly cleaves cellulose into glucose and olygomeric polysaccharides. Hence by selecting samples with cellulosic substance from different locations with extreme environmental conditions like high temperature, high salinity it is possible to isolate cellulolytic bacteria producing cellulase enzymes that are stable at higher processing temperatures and other harsh conditions which is of importance in industrial applications.
 Keywords: Endo-β-1,4-glucanase, Cellulolytic index, Cellulase activity, Thermostable

IGTCM: An integrative genome database of traditional Chinese medicine plants.

Fully understanding traditional Chinese medicines (TCMs) is still challenging because of the extreme complexity of their chemical components and mechanisms of action. The TCM Plant Genome Project aimed to obtain genetic information, determine gene functions, discover regulatory networks of herbal species, and elucidate the molecular mechanisms involved in the disease prevention and treatment, thereby accelerating the modernization of TCMs. A comprehensive database that contains TCM-related information will provide a vital resource. Here, we present an integrative genome database of TCM plants (IGTCM) that contains 14,711,220 records of 83 annotated TCM-related herb genomes, including 3,610,350 genes, 3,534,314 proteins and corresponding coding sequences, and 4,032,242 RNAs, as well as 1033 non-redundant component records for 68 herbs, downloaded and integrated from the GenBank and RefSeq databases. For minimal interconnectivity, each gene, protein, and component was annotated using the eggNOG-mapper tool and Kyoto Encyclopedia of Genes and Genomes database to acquire pathway information and enzyme classifications. These features can be linked across several species and different components. The IGTCM database also provides visualization and sequence similarity search tools for data analyses. These annotated herb genome sequences in IGTCM database are a necessary resource for systematically exploring genes related to the biosynthesis of compounds that have significant medicinal activities and excellent agronomic traits that can be used to improve TCM-related varieties through molecular breeding. It also provides valuable data and tools for future research on drug discovery and the protection and rational use of TCM plant resources. The IGTCM database is freely available at http://yeyn.group:96/.

Mining for Strong Gravitational Lenses with Self-supervised Learning

We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys’ Data Release 9. Targeting the identification of new strong gravitational lens candidates, we first create a rapid similarity search tool to discover new strong lenses given only a single labeled example. We then show how training a simple linear classifier on the self-supervised representations, requiring only a few minutes on a CPU, can automatically classify strong lenses with great efficiency. We present 1192 new strong lens candidates that we identified through a brief visual identification campaign and release an interactive web-based similarity search tool and the top network predictions to facilitate crowd-sourcing rapid discovery of additional strong gravitational lenses and other rare objects: github.com/georgestein/ssl-legacysurvey.

Identification of Constituents and Exploring the Mechanism for Toutongning Capsule in the Treatment of Migraine.

Toutongning capsule (TTNC) is an effective and safe traditional Chinese medicine used in the treatment of migraine. In this present study, a multiscale strategy was used to systematically investigate the mechanism of TTNC in treating migraine, which contained UPLC-UESI-Q Exactive Focus network pharmacology and experimental verification. First, 88 compounds were identified by the UPLC-UESI-Q Exactive Focus method for TTNC. Then, the target fishing for these compounds was performed by means of an efficient drug similarity search tool. Third, a series of network pharmacology experiments were performed to predict the key compounds, targets, and pathways. They were protein-protein interaction (PPI), KEGG pathway enrichment analysis, and herbs-compounds-targets-pathways (H-C-T-P) network construction. As a result, 18 potential key compounds, 20 potential key targets, and 6 potential signaling pathways were obtained for TTNC in treatment with migraine. Finally, molecular docking and experimental were carried out to verify the key targets. In short, the results showed that TTNC is able to treat migraine through multiple components, multiple targets, and multiple pathways. This work may provide a theoretical basis for further research on the molecular mechanism of TTNC in the treatment of migraine.

Paradoxical Pro-angiogenic Effect of Low-Dose Ellipticine Identified by In Silico Drug Repurposing.

Inadequate vessel maintenance or growth causes ischemia in diseases such as myocardial infarction, stroke, and neurodegenerative disorders. Therefore, developing an effective strategy to salvage ischemic tissues using a novel compound is urgent. Drug repurposing has become a widely used method that can make drug discovery more efficient and less expensive. Additionally, computational virtual screening tools make drug discovery faster and more accurate. This study found a novel drug candidate for pro-angiogenesis by in silico virtual screening. Using Gene Expression Omnibus (GEO) microarray datasets related to angiogenesis studies, differentially expressed genes were identified and characteristic direction signatures extracted from GEO2EnrichR were used as input data on L1000CDS2 to screen pro-angiogenic molecules. After a thorough review of the candidates, a list of compounds structurally similar to TWS-119 was generated using ChemMine Tools and its clustering toolbox. ChemMine Tools and ChemminR structural similarity search tools for small-molecule analysis and clustering were used for second screening. A molecular docking simulation was conducted using AutoDock v.4 to evaluate the physicochemical effect of secondary-screened chemicals. A cell viability or toxicity test was performed to determine the proper dose of the final candidate, ellipticine. As a result, we found ellipticine, which has pro-angiogenic effects, using virtual computational methods. The noncytotoxic concentration of ellipticine was 156.25 nM. The phosphorylation of glycogen synthase kinase-3β was decreased, whereas the β-catenin expression was increased in human endothelial cells treated with ellipticine. We concluded that ellipticine at sublethal dosage could be successfully repositioned as a pro-angiogenic substance by in silico virtual screening.

Molecular and functional analysis of naphthalene-degrading bacteria isolated from the effluents of indigenous tanneries.

During present study, four naphthalene- metabolizing bacteria were isolated from tanneries effluents through enrichment on naphthalene as sole carbon source in minimal salt medium. The bacteria were analyzed to document growth pattern, naphthalene removal efficiency, biochemical and molecular characteristics, antibiotic sensitivity, and metabolic profile. The 16S ribosomal RNA gene sequences were compared through BLAST (basic local alignment search tool) similarity search tool and three isolates were found homologous to Brevibacillus agri strain NBRC 15538 and one similar to Burkholderia lata strain 383. The naphthalene removal efficiencies ranged from 1.16 ± 0.056 mg/h (IUBN1) to 1.379 ± 0.021 mg/h (IUBN26). All isolates were positive for p-nitrophenyl phosphate (PO4 ), esculin, and inulin fermentation tests. Majority were positive for glucosaminidase (IUBN3, 17, and 26) and a few for mannitol and sorbitol fermentation (IUBN1). Identification of metabolites through gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry analysis allowed tracing pathways associated with naphthalene degradation. Intermediates such as cis-dihydrodiolnaphthalene, 2-hydroxychromene-2-carboxylate, 6-hydroxyhexanoic acid, acetyl-CoA confirmed that the present study bacteria can metabolize naphthalene through a pathway which differs from the pathways reported in earlier known bacteria. Due to fast growth rates, high naphthalene removal potentials, and multiple degradation pathways, these bacteria can be exploited for bioremediation of naphthalene.

Dig That Lick (DTL): Analyzing Large-Scale Data for Melodic Patterns in Jazz Performances

Dig That Lick (DTL): Analyzing Large-Scale Data for Melodic Patterns in Jazz Performances

New alignment-based sequence extraction software (ALiBaSeq) and its utility for deep level phylogenetics.

Despite many bioinformatic solutions for analyzing sequencing data, few options exist for targeted sequence retrieval from whole genomic sequencing (WGS) data with the ultimate goal of generating a phylogeny. Available tools especially struggle at deep phylogenetic levels and necessitate amino-acid space searches, which may increase rates of false positive results. Many tools are also difficult to install and may lack adequate user resources. Here, we describe a program that uses freely available similarity search tools to find homologs in assembled WGS data with unparalleled freedom to modify parameters. We evaluate its performance compared to other commonly used bioinformatics tools on two divergent insect species (>200 My) for which annotated genomes exist, and on one large set each of highly conserved and more variable loci. Our software is capable of retrieving orthologs from well-curated or unannotated, low or high depth shotgun, and target capture assemblies as well or better than other software as assessed by recovering the most genes with maximal coverage and with a low rate of false positives throughout all datasets. When assessing this combination of criteria, ALiBaSeq is frequently the best evaluated tool for gathering the most comprehensive and accurate phylogenetic alignments on all types of data tested. The software (implemented in Python), tutorials, and manual are freely available at https://github.com/AlexKnyshov/alibaseq.

Deep learning embedder method and tool for mass spectra similarity search

Spectral similarity calculation is widely used in protein identification tools and mass spectra clustering algorithms while comparing theoretical or experimental spectra. The performance of the spectral similarity calculation plays an important role in these tools and algorithms especially in the analysis of large-scale datasets. Recently, deep learning methods have been proposed to improve the performance of clustering algorithms and protein identification by training the algorithms with existing data and the use of multiple spectra and identified peptide features. While the efficiency of these algorithms is still under study in comparison with traditional approaches, their application in proteomics data analysis is becoming more common. Here, we propose the use of deep learning to improve spectral similarity comparison. We assessed the performance of deep learning for spectral similarity, with GLEAMS and a newly trained embedder model (DLEAMSE), which uses high-quality spectra from PRIDE Cluster. Also, we developed a new bioinformatics tool (mslookup - https://github.com/bigbio/DLEAMSE/) that allows users to quickly search for spectra in previously identified mass spectra publish in public repositories and spectral libraries. Finally, we released a human database to enable bioinformaticians and biologists to search for identified spectra in their machines. Significance statementSpectral similarity calculation plays an important role in proteomics data analysis. With deep learning's ability to learn the implicit and effective features from large-scale training datasets, deep learning-based MS/MS spectra embedding models has emerged as a solution to improve mass spectral clustering similarity calculation algorithms. We compare multiple similarity scoring and deep learning methods in terms of accuracy (compute the similarity for a pair of the mass spectrum) and computing-time performance. The benchmark results showed no major differences in accuracy between DLEAMSE and normalized dot product for spectrum similarity calculations. The DLEAMSE GPU implementation is faster than NDP in preprocessing on the GPU server and the similarity calculation of DLEAMSE (Euclidean distance on 32-D vectors) takes about 1/3 of dot product calculations. The deep learning model (DLEAMSE) encoding and embedding steps needed to run once for each spectrum and the embedded 32-D points can be persisted in the repository for future comparison, which is faster for future comparisons and large-scale data. Based on these, we proposed a new tool mslookup that enables the researcher to find spectra previously identified in public data. The tool can be also used to generate in-house databases of previously identified spectra to share with other laboratories and consortiums.

A Critical Guide to BLAST

This Critical Guide in the Introduction to Bioinformatics series provides an overview of the BLAST similarity search tool, briefly examining the underlying algorithm and its rise to popularity. Several Web-based and stand-alone implementations are reviewed, and key features of typical search results are discussed. Specifically, this Guide introduces concepts and theories that underpin the BLAST search tool, and examines features of search outputs important for understanding and interpreting BLAST results. On reading this Guide, users will be able to: search a variety of Web-based sequence databases with different query sequences, and alter search parameters; explain a range of typical search parameters, and the likely impacts on search outputs of changing them; analyse the information conveyed in search outputs and infer the significance of reported matches; examine and investigate the annotations of reported matches, and their provenance; and compare the outputs of different BLAST implementations and evaluate the implications of any differences.

One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome

BackgroundMolecular fingerprints are essential cheminformatics tools for virtual screening and mapping chemical space. Among the different types of fingerprints, substructure fingerprints perform best for small molecules such as drugs, while atom-pair fingerprints are preferable for large molecules such as peptides. However, no available fingerprint achieves good performance on both classes of molecules.ResultsHere we set out to design a new fingerprint suitable for both small and large molecules by combining substructure and atom-pair concepts. Our quest resulted in a new fingerprint called MinHashed atom-pair fingerprint up to a diameter of four bonds (MAP4). In this fingerprint the circular substructures with radii of r = 1 and r = 2 bonds around each atom in an atom-pair are written as two pairs of SMILES, each pair being combined with the topological distance separating the two central atoms. These so-called atom-pair molecular shingles are hashed, and the resulting set of hashes is MinHashed to form the MAP4 fingerprint. MAP4 significantly outperforms all other fingerprints on an extended benchmark that combines the Riniker and Landrum small molecule benchmark with a peptide benchmark recovering BLAST analogs from either scrambled or point mutation analogs. MAP4 furthermore produces well-organized chemical space tree-maps (TMAPs) for databases as diverse as DrugBank, ChEMBL, SwissProt and the Human Metabolome Database (HMBD), and differentiates between all metabolites in HMBD, over 70% of which are indistinguishable from their nearest neighbor using substructure fingerprints.ConclusionMAP4 is a new molecular fingerprint suitable for drugs, biomolecules, and the metabolome and can be adopted as a universal fingerprint to describe and search chemical space. The source code is available at https://github.com/reymond-group/map4 and interactive MAP4 similarity search tools and TMAPs for various databases are accessible at http://map-search.gdb.tools/ and http://tm.gdb.tools/map4/.

COSS: A Fast and User-Friendly Tool for Spectral Library Searching.

Spectral similarity searching to identify peptide-derived MS/MS spectra is a promising technique, and different spectrum similarity search tools have therefore been developed. Each of these tools, however, comes with some limitations, mainly because of low processing speed and issues with handling large databases. Furthermore, the number of spectral data formats supported is typically limited, which also creates a threshold to adoption. We have therefore developed COSS (CompOmics Spectral Searching), a new and user-friendly spectral library search tool supporting two scoring functions. COSS also includes decoy spectra generation for result validation. We have benchmarked COSS on three different spectral libraries and compared the results with established spectral searching tools and a sequence database search tool. Our comparison showed that COSS more reliably identifies spectra, is capable of handling large data sets and libraries, and is an easy to use tool that can run on low computer specifications. COSS binaries and source code can be freely downloaded from https://github.com/compomics/COSS.

The chemfp project

The chemfp project has had four main goals: (1) promote the FPS format as a text-based exchange format for dense binary cheminformatics fingerprints, (2) develop a high-performance implementation of the BitBound algorithm that could be used as an effective baseline to benchmark new similarity search implementations, (3) experiment with funding a pure open source software project through commercial sales, and (4) publish the results and lessons learned as a guide for future implementors. The FPS format has had only minor success, though it did influence development of the FPB binary format, which is faster to load but more complex. Both are summarized. The chemfp benchmark and the no-cost/open source version of chemfp are proposed as a reference baseline to evaluate the effectiveness of other similarity search tools. They are used to evaluate the faster commercial version of chemfp, which can test 130 million 1024-bit fingerprint Tanimotos per second on a single core of a standard x86-64 server machine. When combined with the BitBound algorithm, a k = 1000 nearest-neighbor search of the 1.8 million 2048-bit Morgan fingerprints of ChEMBL 24 averages 27 ms/query. The same search of 970 million PubChem fingerprints averages 220 ms/query, making chemfp one of the fastest CPU-based similarity search implementations. Modern CPUs are fast enough that memory bandwidth and latency are now important factors. Single-threaded search uses most of the available memory bandwidth. Sorting the fingerprints by popcount improves memory coherency, which when combined with 4 OpenMP threads makes it possible to construct an N × N similarity matrix for 1 million fingerprints in about 30 min. These observations may affect the interpretation of previous publications which assumed that search was strongly CPU bound. The chemfp project funding came from selling a purely open-source software product. Several product business models were tried, but none proved sustainable. Some of the experiences are discussed, in order to contribute to the ongoing conversation on the role of open source software in cheminformatics.

CLASTR: The Cellosaurus STR similarity search tool - A precious help for cell line authentication.

Despite an increased awareness of the problematic of cell line cross-contamination and misidentification, it remains nowadays a major source of erroneous experimental results in biomedical research. To prevent it, researchers are expected to frequently test the authenticity of the cell lines they are working on. STR profiling was selected as the international reference method to perform cell line authentication. While the experimental protocols and manipulations for generating a STR profile are well described, the available tools and workflows to analyze such data are lacking. The Cellosaurus knowledge resource aimed to improve the situation by compiling all the publicly available STR profiles from the literature and other databases. As a result, it grew to become the largest database in terms of human STR profiles, with 6,474 distinct cell lines having an associated STR profile (release July 31, 2019). Here we present CLASTR, the Cellosaurus STR similarity search tool enabling users to compare one or more STR profiles with those available in the Cellosaurus cell line knowledge resource. It aims to help researchers in the process of cell line authentication by providing numerous functionalities. The tool is publicly accessible on the SIB ExPASy server (https://web.expasy.org/cellosaurus-str-search) and its source code is available on GitHub under the GPL-3.0 license.

RAFTS3G: an efficient and versatile clustering software to analyses in large protein datasets

BackgroundClustering methods are essential to partitioning biological samples being useful to minimize the information complexity in large datasets. Tools in this context usually generates data with greed algorithms that solves some Data Mining difficulties which can degrade biological relevant information during the clustering process. The lack of standardization of metrics and consistent bases also raises questions about the clustering efficiency of some methods. Benchmarks are needed to explore the full potential of clustering methods - in which alignment-free methods stand out - and the good choice of dataset makes it essentials.ResultsHere we present a new approach to Data Mining in large protein sequences datasets, the Rapid Alignment Free Tool for Sequences Similarity Search to Groups (RAFTS3G), a method to clustering aiming of losing less biological information in the processes of generation groups. The strategy developed in our algorithm is optimized to be more astringent which reflects increase in accuracy and sensitivity in the generation of clusters in a wide range of similarity. RAFTS3G is the better choice compared to three main methods when the user wants more reliable result even ignoring the ideal threshold to clustering.ConclusionIn general, RAFTS3G is able to group up to millions of biological sequences into large datasets, which is a remarkable option of efficiency in clustering. RAFTS3G compared to other “standard-gold” methods in the clustering of large biological data maintains the balance between the reduction of biological information redundancy and the creation of consistent groups. We bring the binary search concept applied to grouped sequences which shows maintaining sensitivity/accuracy relation and up to minimize the time of data generated with RAFTS3G process.

Isolation and Identification of Three Novel Antioxidant Peptides from the Bactrian Camel Milk Hydrolysates

The aim of this study was isolation and purification of antioxidant peptides from Bactrian camel milk (BCM) hydrolysate. Trypsin, pepsin, alcalase, and papain were used for hydrolysis of BCM. The peptides fraction from trypsin digest exhibited a strong antioxidant activity among them, and it was separated by ultrafiltration (3000 Da membrane). The resulting peptides (< 3000 Da) were further separated with using gel filtration chromatography (HW-55F) and reverse-phase high-performance liquid chromatography (RP-HPLC). Three novel antioxidant peptides were purified and sequenced by MALDI TOF–MS/MS as RLDGQGRPRVWLGR (TFI-b1), TPDNIDIWLGGIAEPQVKR (TFI-b2) and VAYSDDGENWTEYRDQGAVEGK (TFI-b3) with molecular weights 1665.94, 2122.13, and 2489.09 Da, respectively. The IC50 values of these peptides on DPPH were 1.9, 1.2 and 0.6 mg/mL and on ABTS are 2.4, 1.8 and 0.9 mg/mL, respectively. The peptides identified using similarity search tool of SIB ExPASy Bioinformatics Resources Portal (http://www.expasy.org).