PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data.

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

SWIGH-SCORE: A translational light-weight approach in computational detection of rearranged immunoglobulin heavy chain to be used in monoclonal lymphoproliferative disorders.

We present a lightweight tool for clonotyping and measurable residual disease (MRD) assessment in monoclonal lymphoproliferative disorders. It is a translational method that enables computational detection of rearranged immunoglobulin heavy chain gene sequences.•The swigh-score clonotyping tool emphasizes parallelization and applicability across sequencing platforms.•The algorithm is based on an adaptation of the Smith-Waterman algorithm for local alignment of reads generated by 2nd and 3rd generation of sequencers.For method validation, we demonstrate the targeted sequences of immunoglobulin heavy chain genes from diagnostic bone marrow using serial dilutions of CD138+ plasma cells from a patient with multiple myeloma. Sequencing libraries from diagnostic samples were prepared for the three sequencing platforms, Ion S5 (Thermo Fisher Scientific), MiSeq (Illumina), and MinION (Oxford Nanopore), using the LymphoTrack assay. Basic quality filtering was performed, and a Smith-Waterman-based swigh-score algorithm was developed in shell and C for clonotyping and MRD assessment using FASTQ data files. Performance is demonstrated across the three different sequencing platforms.

Profiling population-wide exposure to environmental chemicals: A case study of naphthalene.

Long-term exposure to environmental chemicals can detrimentally impact human health, and understanding the relationship between age distribution and levels of external and internal exposure is crucial. Nonetheless, existing methods for assessing population-wide exposure across age groups are limited. To bridge this research gap, we introduced a modeling approach designed to assess both chronic external and internal exposure to chemicals at the population level. The external and internal exposure assessments were quantified in terms of the average daily dose (ADD) and steady-state blood concentration of the environmental chemical, respectively, which were categorized by age and gender groups. The modeling process was presented within a spreadsheet framework, affording users the capability to execute population-wide exposure analyses across a spectrum of chemicals. Our simulation outcomes underscored a salient trend: younger age groups, particularly infants and children, exhibited markedly higher ADD values and blood concentrations of environmental chemicals compared to their older counterparts. This observation is due to the elevated basal metabolic rate per unit of body weight characteristic of younger individuals, coupled with their diminished biotransformation kinetics of xenobiotics within their livers. These factors collectively contribute to increased intake rates of environmental chemicals per unit of body weight through air and food consumption, along with heightened bioaccumulation of these chemicals within their bodies (e.g., blood). Furthermore, we augmented the precision of the external and internal exposure assessment by incorporating the age distribution across the population. The simulation outcomes unveiled that, to estimate the central tendency of the population's exposure levels, employing the baseline value group (age group 21-30) or the surrogate age of 25 serves as a simple yet dependable approach. However, for comprehensive population protection, our recommendation aligns with conducting exposure assessments for the younger age groups (age group 0-11). Future studies should integrate individual-level exposure assessment, analyze vulnerable population groups, and refine population structures within our developed model.

Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators.

Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately identifies senescent cells in both bulk and single-cell transcriptome. Trained on 602 samples from 52 senescence transcriptome datasets spanning 30 cell types, SenCID identifies six major senescence identities (SIDs). Different SIDs exhibit different senescence baselines, stemness, gene functions, and responses to senolytics. SenCID enables the reconstruction of senescent trajectories under normal aging, chronic diseases, and COVID-19. Additionally, when applied to single-cell Perturb-seq data, SenCID helps reveal a hierarchy of senescence modulators. Overall, SenCID is an essential tool for precise single-cell analysis of cellular senescence, enabling targeted interventions against senescent cells.

Computational tools and algorithms for ion mobility spectrometry-mass spectrometry.

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.

Neoantigen identification: Technological advances and challenges.

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

CellRemorph: A Toolkit for Transforming, Selecting, and Slicing 3D Cell Structures on the Road to Morphologically Detailed Astrocyte Simulations.

Understanding functions of astrocytes can be greatly enhanced by building and simulating computational models that capture their morphological details. Novel computational tools enable utilization of existing morphological data of astrocytes and building models that have appropriate level of details for specific simulation purposes. In addition to analyzing existing computational tools for constructing, transforming, and assessing astrocyte morphologies, we present here the CellRemorph toolkit implemented as an add-on for Blender, a 3D modeling platform increasingly recognized for its utility for manipulating 3D biological data. To our knowledge, CellRemorph is the first toolkit for transforming astrocyte morphologies from polygonal surface meshes into adjustable surface point clouds and vice versa, precisely selecting nanoprocesses, and slicing morphologies into segments with equal surface areas or volumes. CellRemorph is an open-source toolkit under the GNU General Public License and easily accessible via an intuitive graphical user interface. CellRemorph will be a valuable addition to other Blender add-ons, providing novel functionality that facilitates the creation of realistic astrocyte morphologies for different types of morphologically detailed simulations elucidating the role of astrocytes both in health and disease.

SynBioTools: a one-stop facility for searching and selecting synthetic biology tools.

BACKGROUND: The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology. RESULTS: We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system. CONCLUSIONS: SynBioTools is freely available at https://synbiotools.lifesynther.com/ . It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection.

Application of Computational Screening Tools and Nanotechnology for Enhanced Drug Synergism in Cancer Therapy.

BACKGROUND: Chemoresistance continues to limit the recovery of patients with cancer. New strategies, such as combination therapy or nanotechnology, can be further improved. OBJECTIVE: In this study, we applied the computational strategy by exploiting two databases (CellMiner and Prism) to sort out the cell lines sensitive to both anti-cancer drugs, paclitaxel (PTX) and dihydroartemisinin (DHA); both of which are potentially synergistic in several cell lines. METHODS: The combination of PTX and DHA was screened at different ratios to select the optimal ratio that could inhibit lung adenocarcinoma NCI-H23 the most. To further enhance therapeutic efficacy, these combinations of drugs were incorporated into a nanosystem. RESULTS: At a PTX:DHA ratio of 1:2 (w/w), the combined drugs obtained the best combination index (0.84), indicating a synergistic effect. The drug-loaded nanoparticles sized at 135 nm with the drug loading capacity of 15.5 ± 1.34 and 13.8 ± 0.56 corresponding to DHA and PTX, respectively, were used. The nano-sized particles improved drug internalization into the cells, resulting in the significant inhibition of cell growth at all tested concentrations (p < 0.001). Additionally, α-tubulin aggregation, DNA damage suggested the molecular mechanism behind cell death upon PTX-DHA-loaded nanoparticle treatment. Moreover, the rate of apoptosis increased from approximately 5% to more than 20%, and the expression of apoptotic proteins changed 4 and 3 folds corresponding to p-53 and Bcl-2, respectively. CONCLUSION: This study was designed thoroughly by screening cell lines for the optimization of formulations. This novel approach could pave the way for the selection of combined drugs for precise cancer treatment.

Computational Tools and Resources for CRISPR/Cas Genome Editing.

The past decade has witnessed a rapid evolution in identifying more versatile clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) nucleases and their functional variants, as well as in developing precise CRISPR/Cas-derived genome editors. The programmable and robust features of the genome editors provide an effective RNA-guided platform for fundamental life science research and subsequent applications in diverse scenarios, including biomedical innovation and targeted crop improvement. One of the most essential principles is to guide alterations in genomic sequences or genes in the intended manner without undesired off-target impacts, which strongly depends on the efficiency and specificity of single guide RNA (sgRNA)-directed recognition of targeted DNA sequences. Recent advances in empirical scoring algorithms and machine learning models have facilitated sgRNA design and off-target prediction. In this review, we first briefly introduce the different features of CRISPR/Cas tools that should be taken into consideration to achieve specific purposes. Secondly, we focus on the computer-assisted tools and resources that are widely used in designing sgRNAs and analyzing CRISPR/Cas-induced on- and off-target mutations. Thirdly, we provide insights into the limitations of available computational tools that would help researchers of this field for further optimization. Lastly, we suggest a simple but effective workflow for choosing and applying web-based resources and tools for CRISPR/Cas genome editing.

Computational Methods for Single-cell DNA Methylome Analysis.

Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.

TrackUSF, a novel tool for automated ultrasonic vocalization analysis, reveals modified calls in a rat model of autism.

BACKGROUND: Various mammalian species emit ultrasonic vocalizations (USVs), which reflect their emotional state and mediate social interactions. USVs are usually analyzed by manual or semi-automated methodologies that categorize discrete USVs according to their structure in the frequency-time domains. This laborious analysis hinders the effective use of USVs as a readout for high-throughput analysis of behavioral changes in animals. RESULTS: Here we present a novel automated open-source tool that utilizes a different approach towards USV analysis, termed TrackUSF. To validate TrackUSF, we analyzed calls from different animal species, namely mice, rats, and bats, recorded in various settings and compared the results with a manual analysis by a trained observer. We found that TrackUSF detected the majority of USVs, with less than 1% of false-positive detections. We then employed TrackUSF to analyze social vocalizations in Shank3-deficient rats, a rat model of autism, and revealed that these vocalizations exhibit a spectrum of deviations from appetitive calls towards aversive calls. CONCLUSIONS: TrackUSF is a simple and easy-to-use system that may be used for a high-throughput comparison of ultrasonic vocalizations between groups of animals of any kind in any setting, with no prior assumptions.

Correlation between the binding affinity and the conformational entropy of nanobody SARS-CoV-2 spike protein complexes.

Camelid single-domain antibodies, also known as nanobodies, can be readily isolated from naïve libraries for specific targets but often bind too weakly to their targets to be immediately useful. Laboratory-based genetic engineering methods to enhance their affinity, termed maturation, can deliver useful reagents for different areas of biology and potentially medicine. Using the receptor binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and a naïve library, we generated closely related nanobodies with micromolar to nanomolar binding affinities. By analyzing the structure-activity relationship using X-ray crystallography, cryoelectron microscopy, and biophysical methods, we observed that higher conformational entropy losses in the formation of the spike protein-nanobody complex are associated with tighter binding. To investigate this, we generated structural ensembles of the different complexes from electron microscopy maps and correlated the conformational fluctuations with binding affinity. This insight guided the engineering of a nanobody with improved affinity for the spike protein.

Gattaca: Base-Pair Resolution Mutation Tracking for Somatic Evolution Studies using Agent-based Models.

Research over the past two decades has made substantial inroads into our understanding of somatic mutations. Recently, these studies have focused on understanding their presence in homeostatic tissue. In parallel, agent-based mechanistic models have emerged as an important tool for understanding somatic mutation in tissue; yet no common methodology currently exists to provide base-pair resolution data for these models. Here, we present Gattaca as the first method for introducing and tracking somatic mutations at the base-pair resolution within agent-based models that typically lack nuclei. With nuclei that incorporate human reference genomes, mutational context, and sequence coverage/error information, Gattaca is able to realistically evolve sequence data, facilitating comparisons between in silico cell tissue modeling with experimental human somatic mutation data. This user-friendly method, incorporated into each in silico cell, allows us to fully capture somatic mutation spectra and evolution.

Determination of tissue contributions to the circulating lipid pool in cold exposure via systematic assessment of lipid profiles.

Plasma lipid levels are altered in chronic conditions such as type 2 diabetes and cardiovascular disease as well as during acute stresses such as fasting and cold exposure. Advances in MS-based lipidomics have uncovered a complex plasma lipidome of more than 500 lipids that serve functional roles, including as energy substrates and signaling molecules. This plasma lipid pool is maintained through regulation of tissue production, secretion, and uptake. A major challenge in understanding the lipidome complexity is establishing the tissues of origin and uptake for various plasma lipids, which is valuable for determining lipid functions. Using cold exposure as an acute stress, we performed global lipidomics on plasma and in nine tissues that may contribute to the circulating lipid pool. We found that numerous species of plasma acylcarnitines (ACars) and ceramides (Cers) were significantly altered upon cold exposure. Through computational assessment, we identified the liver and brown adipose tissue as major contributors and consumers of circulating ACars, in agreement with our previous work. We further identified the kidney and intestine as novel contributors to the circulating ACar pool and validated these findings with gene expression analysis. Regression analysis also identified that the brown adipose tissue and kidney are interactors with the plasma Cer pool. Taken together, these studies provide an adaptable computational tool to assess tissue contribution to the plasma lipid pool. Our findings have further implications in understanding the function of plasma ACars and Cers, which are elevated in metabolic diseases.

Computational Identification of Stearic Acid as a Potential PDK1 Inhibitor and In Vitro Validation of Stearic Acid as Colon Cancer Therapeutic in Combination with 5-Fluorouracil.

BACKGROUND: Colorectal cancer is the third largest cause of cancer-related mortality worldwide. Although current treatments with chemotherapeutics have allowed for management of colorectal cancer, additional novel treatments are essential. Intervening with the metabolic reprogramming observed in cancers called "Warburg Effect," is one of the novel strategies considered to combat cancers. In the metabolic reprogramming pathway, pyruvate dehydrogenase kinase (PDK1) plays a pivotal role. Identification and characterization of a PDK1 inhibitor is of paramount importance. Further, for efficacious treatment of colorectal cancers, combinatorial regimens are essential. To this end, we opted to identify a PDK1 inhibitor using computational structure-based drug design FINDSITEcomb and perform combinatorial studies with 5-FU for efficacious treatment of colorectal cancers. METHODS: Using computational structure-based drug design FINDSITEcomb, stearic acid (SA) was identified as a possible PDK1 inhibitor. Elucidation of the mechanism of action of SA was performed using flow cytometry, clonogenic assays. RESULTS: When the growth inhibitory potential of SA was tested on colorectal adenocarcinoma (DLD-1) cells, a 50% inhibitory concentration (IC50) of 60 µM was recorded. Moreover, SA inhibited the proliferation potential of DLD-1 cells as shown by the clonogenic assay and there was a sustained response even after withdrawal of the compound. Elucidation of the mechanism of action revealed, that the inhibitory effect of SA was through the programmed cell death pathway. There was increase in the number of apoptotic and multicaspase positive cells. SA also impacted the levels of the cell survival protein Bcl-2. With the aim of achieving improved treatment for colorectal cancer, we opted to combine 5-fluorouracil (5-FU), the currently used drug in the clinic, with SA. Combining SA with 5-FU, revealed a synergistic effect in which the IC50 of 5-FU decreased from 25 to 6 µM upon combination with 60 µM SA. Further, SA did not inhibit non-tumorigenic NIH-3T3 proliferation. CONCLUSIONS: We envision that this significant decrease in the IC50 of 5-FU could translate into less side effects of 5-FU and increase the efficacy of the treatment due to the multifaceted action of SA. The data generated from the current studies on the inhibition of colorectal adenocarcinoma by SA discovered by the use of the computational program as well as synergistic action with 5-FU should open up novel therapeutic options for the management of colorectal adenocarcinomas.

Design and Development of a Computational Tool for a Dialyzer by Using Computational Fluid Dynamic (CFD) Model.

In order to reduce the hemodialysis cost and duration, an investigation of the effect of dialyzer design and process variables on the solute clearance rate is required. It is not easy to translate the in vivo transfer process with in vitro experiments, as it involves a high cost to produce various designs and membranes for the dialyzer. The primary objective of this study was the design and development of a computational tool for a dialyzer by using a computational fluid dynamic (CFD) model. Due to their complexity, only researchers with expertise in computational analysis can use dialyzer models. Therefore, COMSOL Inc. (Stockholm, Sweden) has made an application on membrane dialysis to study the impact of different design and process parameters on dialyzed liquid concentration. Still, membrane mathematical modeling is not considered in this application. This void hinders an investigation of the impact of membrane characteristics on the solute clearance rate. This study has developed a stand-alone computational tool in COMSOL Multiphysics 5.4 to fill this void. A review of the literature conducted shows that there are no suitable stand-alone computational tools for kidney dialysis. Very little work has been undertaken to validate the stand-alone computational tool. Medical staff in the hospitals require a computational tool that can be installed quickly and provide results with limited knowledge of dialysis. This work aims to construct a user-friendly computational tool to solve this problem. The development of a user-friendly stand-alone computational tool for the dialyzer is described thoroughly. This application simulates a mathematical model with the Finite Element Method using the COMSOL Multiphysics solver. The software tool is converted to a stand-alone version with the COMSOL compiler. The stand-alone computational tool provides the clearance rate of six different toxins and module packing density. Compared with the previous application, the stand-alone computational tool of membrane dialysis enables the user to investigate the impact of membrane characteristics and process parameters on the clearance rate of different solutes. The results are also inconsistent with the literature data, and the differences ranges are 0.09-6.35% and 0.22-2.63% for urea clearance rate and glucose clearance rate, respectively. Statistical analysis of the results is presented as mean with 95% confidence intervals (CIs) and p values 0.9472 and 0.833 of the urea and glucose clearance rates, respectively.

Analysis of Aneuploidy Spectrum From Whole-Genome Sequencing Provides Rapid Assessment of Clonal Variation Within Established Cancer Cell Lines.

BACKGROUND: The revolution in next-generation sequencing (NGS) technology has allowed easy access and sharing of high-throughput sequencing datasets of cancer cell lines and their integrative analyses. However, long-term passaging and culture conditions introduce high levels of genomic and phenotypic diversity in established cell lines resulting in strain differences. Thus, clonal variation in cultured cell lines with respect to the reference standard is a major barrier in systems biology data analyses. Therefore, there is a pressing need for a fast and entry-level assessment of clonal variations within cell lines using their high-throughput sequencing data. RESULTS: We developed a Python-based software, AStra, for de novo estimation of the genome-wide segmental aneuploidy to measure and visually interpret strain-level similarities or differences of cancer cell lines from whole-genome sequencing (WGS). We demonstrated that aneuploidy spectrum can capture the genetic variations in 27 strains of MCF7 breast cancer cell line collected from different laboratories. Performance evaluation of AStra using several cancer sequencing datasets revealed that cancer cell lines exhibit distinct aneuploidy spectra which reflect their previously-reported karyotypic observations. Similarly, AStra successfully identified large-scale DNA copy number variations (CNVs) artificially introduced in simulated WGS datasets. CONCLUSIONS: AStra provides an analytical and visualization platform for rapid and easy comparison between different strains or between cell lines based on their aneuploidy spectra solely using the raw BAM files representing mapped reads. We recommend AStra for rapid first-pass quality assessment of cancer cell lines before integrating scientific datasets that employ deep sequencing. AStra is an open-source software and is available at https://github.com/AISKhalil/AStra.

In silico characterization of the GH5-cellulase family from uncultured microorganisms: physicochemical and structural studies.

BACKGROUND: Hydrolysis of cellulose-based biomass by cellulases produce fermented sugar for making biofuels, such as bioethanol. Cellulases hydrolyze the ß-1,4-glycosidic linkage of cellulose and can be obtained from cultured and uncultured microorganisms. Uncultured microorganisms are a source for exploring novel cellulase genes through the metagenomic approach. Metagenomics concerns the extraction, cloning, and analysis of the entire genetic complement of a habitat without cultivating microbes. The glycoside hydrolase 5 family (GH5) is a cellulase family, as the largest group of glycoside hydrolases. Numerous variants of GH5-cellulase family have been identified through the metagenomic approach, including CelGH5 in this study. University-CoE-Research Center for Biomolecule Engineering, Universitas Airlangga successfully isolated CelGH5 from waste decomposition of oil palm empty fruit bunches (OPEFB) soil by metagenomics approach. The properties and structural characteristics of GH5-cellulases from uncultured microorganisms can be studied using computational tools and software. RESULTS: The GH5-cellulase family from uncultured microorganisms was characterized using standard computational-based tools. The amino acid sequences and 3D-protein structures were retrieved from the GenBank Database and Protein Data Bank. The physicochemical analysis revealed the sequence length was roughly 332-751 amino acids, with the molecular weight range around 37-83 kDa, dominantly negative charges with pI values below 7. Alanine was the most abundant amino acid making up the GH5-cellulase family and the percentage of hydrophobic amino acids was more than hydrophilic. Interestingly, ten endopeptidases with the highest average number of cleavage sites were found. Another uniqueness demonstrated that there was also a difference in stability between in silico and wet lab. The II values indicated CelGH5 and ACA61162.1 as unstable enzymes, while the wet lab showed they were stable at broad pH range. The program of SOPMA, PDBsum, ProSA, and SAVES provided the secondary and tertiary structure analysis. The predominant secondary structure was the random coil, and tertiary structure has fulfilled the structure quality of QMEAN4, ERRAT, Ramachandran plot, and Z score. CONCLUSION: This study can afford the new insights about the physicochemical and structural properties of the GH5-cellulase family from uncultured microorganisms. Furthermore, in silico analysis could be valuable in selecting a highly efficient cellulases for enhanced enzyme production.