Post-genomic Era Research Articles

Perturbation-specific transcriptional mapping for unbiased target elucidation of antibiotics

The rising prevalence of antibiotic resistance threatens human health. While more sophisticated strategies for antibiotic discovery are being developed, target elucidation of new chemical entities remains challenging. In the postgenomic era, expression profiling can play an important role in mechanism-of-action (MOA) prediction by reporting on the cellular response to perturbation. However, the broad application of transcriptomics has yet to fulfill its promise of transforming target elucidation due to challenges in identifying the most relevant, direct responses to target inhibition. We developed an unbiased strategy for MOA prediction, called perturbation-specific transcriptional mapping (PerSpecTM), in which large-throughput expression profiling of wild-type or hypomorphic mutants, depleted for essential targets, enables a computational strategy to address this challenge. We applied PerSpecTM to perform reference-based MOA prediction based on the principle that similar perturbations, whether chemical or genetic, will elicit similar transcriptional responses. Using this approach, we elucidated the MOAs of three molecules with activity against Pseudomonas aeruginosa by comparing their expression profiles to those of a reference set of antimicrobial compounds with known MOAs. We also show that transcriptional responses to small-molecule inhibition resemble those resulting from genetic depletion of essential targets by clustered regularly interspaced short palindromic repeats interference (CRISPRi) by PerSpecTM, demonstrating proof of concept that correlations between expression profiles of small-molecule and genetic perturbations can facilitate MOA prediction when no chemical entities exist to serve as a reference. Empowered by PerSpecTM, this work lays the foundation for an unbiased, readily scalable, systematic reference-based strategy for MOA elucidation that could transform antibiotic discovery efforts.

Genetic research and precision medicine practice of childhood epilepsy in the post-genomic era

Genetic research and precision medicine practice of childhood epilepsy in the post-genomic era

The current status and improvement directions of legal rules regarding Chinese national gene banks for farm animal genetic resources.

A gene bank for farm animal genetic resources (FAGR) is an important facility for the diversity conservation of FAGR. The primary purpose of gene banks for FAGR is the reconstruction of those breeds. With the advent of the post-genomic era, gene banks for FAGR have increasingly become an infrastructure for the support of livestock research and animal breeding, and a platform for international research collaboration. China is one of the richest countries in the world in terms of FAGR. Chinese National Gene Banks for FAGR (CNGBFs) have an important legal status and play an important function in the Chinese FAGR protection system. In this paper, we reviewed the current situation of CNGBFs construction, and systematically collected and analyzed legal rules related to CNGBFs. As results, those legal rules were categorized into two types: (a) organization and management, (b) activities. We summarized problems existing in the current legal rules for CNGBFs from three levels: institution, practical operation, and digital development. Improvement directions of legal rules regarding CNGBFs are proposed. They include clarifying the utilization function of CNGBFs and the ownership of FAGR in CNGBFs. Moreover, improving the mechanism of administrative management, rules on domestic and international access and benefit-sharing, and the system of digital sequence information management are also suggested. The improvements in those legal rules will contribute to the appropriate utilization of Chinese FAGR and international collaborations.

Orientia tsutsugamushi infection reduces host gluconeogenic but not glycolytic substrates.

Orientia tsutsugamushi a causal agent of scrub typhus, is an obligate intracellular bacterium that, akin to other rickettsiae, is dependent on host cell-derived nutrients for survival and thus pathogenesis. Based on limited experimental evidence and genome-based in silico predictions, O. tsutsugamushi is hypothesized to parasitize host central carbon metabolism (CCM). Here, we (re-)evaluated O. tsutsugamushi dependency on host cell CCM as initiated by glucose and glutamine. Orientia infection had no effect on host glucose and glutamine consumption or lactate accumulation, indicating no change in overall flux through CCM. However, host cell mitochondrial activity and ATP levels were reduced during infection and correspond with lower intracellular glutamine and glutamate pools. To further probe the essentiality of host CCM in O. tsutsugamushi proliferation, we developed a minimal medium for host cell cultivation and paired it with chemical inhibitors to restrict the intermediates and processes related to glucose and glutamine metabolism. These conditions failed to negatively impact O. tsutsugamushi intracellular growth, suggesting the bacterium is adept at scavenging from host CCM. Accordingly, untargeted metabolomics was utilized to evaluate minor changes in host CCM metabolic intermediates across O. tsutsugamushi infection and revealed that pathogen proliferation corresponds with reductions in critical CCM building blocks, including amino acids and TCA cycle intermediates, as well as increases in lipid catabolism. This study directly correlates O. tsutsugamushi proliferation to alterations in host CCM and identifies metabolic intermediates that are likely critical for pathogen fitness.IMPORTANCEObligate intracellular bacterial pathogens have evolved strategies to reside and proliferate within the eukaryotic intracellular environment. At the crux of this parasitism is the balance between host and pathogen metabolic requirements. The physiological basis driving O. tsutsugamushi dependency on its mammalian host remains undefined. By evaluating alterations in host metabolism during O. tsutsugamushi proliferation, we discovered that bacterial growth is independent of the host's nutritional environment but appears dependent on host gluconeogenic substrates, including amino acids. Given that O. tsutsugamushi replication is essential for its virulence, this study provides experimental evidence for the first time in the post-genomic era of metabolic intermediates potentially parasitized by a scrub typhus agent.

323 Advances in using membrane vesicles for studying intestinal functions

Abstract Enterocytes are the major cell lineage of the small intestinal epithelia along the crypt-villus axis in gut mucosa. Enterocytes participate in the gut terminal nutrient digestion and absorption; and have essential roles in nutrient and metabolite recycling and efflux, nutrient and hormonal sensing, interactions with symbiotic microbiome and binding to infectious viruses, thus influencing the host homeostasis and whole-body physiological status. Intestinal membrane vesicles have been widely used for studying various gut functions in the past several decades. Villus enterocytic brush border membrane and the apical membrane vesicles along the crypt-villus axis in gut mucosa can be prepared and isolated by Mg2+- and Ca2+-precipitation and differential centrifugation procedures. Whereas, gut mucosal basolateral membrane vesicles can be prepared, fractionated and differentiated from other intracellular organelles such as the mitochondrial inner membrane by the Percoll gradient ultracentrifugation. Nutrient transport and exchange activities in the membrane vesicles in vitro are conducted often by rapid hand filtration, and in excess, in an automated fast sampling apparatus. Nutrient transport and exchange activity kinetics are derived according to the well-established classic Michaelis-Menten and the tracer inhibitory kinetic models with a simple linear diffusional component. Physiological and biochemical properties associated with various nutrient uptake systems such as pH optimum, requirements for co-transported ions (e.g., Na+, K+, Cl-), transporter affinity and substrate specificity can be effectively investigated in vitro with the membrane vesicles by altering the compositions of uptake buffers and intra-vesicular pre-loading buffers. Some of the fundamental gut functions are established via using the membrane vesicles in different mammalian species. And these include H+-peptide cotransport, Na+-cotransport and Na+-independent systems of hexose uptake across the apical membrane and the low-affinity Na+-independent hexose bidirectional uptake across the basolateral membrane that were characterized in rodents; Na+-dependent cotransport and Na+-independent systems of amino acid (AA) transport across the apical membrane that were revealed in rodents and pigs; as well as the Na+-hexose and AA cotransport and the co-expression of the coronavirus SARS-CoV-2 host epithelial apical membrane binding receptor, i.e., the angiotensin-converting enzyme-2 (ACE2), along the gut crypt-villus axis that were identified in liquid formula-fed piglets. In the post genomic and metagenomic sequencing era, further research efforts need to pursue understanding of expression and various functionality of the gut terminal hydrolases, nutrient transporters, nutrient exchangers, nutrient sensors, substrate and particle receptors, taste receptors, viral binding receptors such as ACE2 and hormonal receptors and their interactions with luminal factors such as nutrient, metabolites, symbiotic bacterial outer membrane vesicles etc., along the longitudinal and crypt-villus axes, through using the mammalian primary enterocytic apical and the basolateral membrane vesicles under various research settings.

Distribution rules of 8-mer spectra and characterization of evolution state in animal genome sequences

BackgroundStudying the composition rules and evolution mechanisms of genome sequences are core issues in the post-genomic era, and k-mer spectrum analysis of genome sequences is an effective means to solve this problem.ResultWe divided total 8-mers of genome sequences into 16 kinds of XY-type due to XY dinucleotides number in 8-mers. Previous works explored that the independent unimodal distributions observed only in three CG-type 8-mer spectra, while non-CG type 8-mer spectra have not the universal phenomenon from prokaryotes to eukaryotes. On this basis, we analyzed the distribution variation of non-CG type 8-mer spectra across 889 animal genome sequences. Following the evolutionary order of animals from primitive to more complex, we found that the spectrum distributions gradually transition from unimodal to tri-modal. The relative distance from the average frequency of each non-CG type 8-mers to the center frequency is different within a species and among different species. For the 8-mers contain CG dinucleotides, we further divided these into 16 subsets, where each 8-mer contains both CG and XY dinucleotides, called XY1_CG1 subsets. We found that the separability values of XY1_CG1 spectra are closely related to the evolution and specificity of animals. Considering the constraint of Chargaff’s second parity rule, we finally obtained 10 separability values as the feature set to characterize the evolution state of genome sequences. In order to verify the rationality of the feature set, we used 14 common classification algorithms to perform binary classification tests. The results showed that the accuracy (Acc) ranged between 98.70% and 83.88% among birds, other vertebrates and mammals.ConclusionWe proposed a credible feature set to characterizes the evolution state of genomes and obtained satisfied results by the feature set on large scale classification of animals.

A Review of the Regulatory Challenges of Personalized Medicine.

Personalized medicine integrates genomics with clinical and familial histories, revolutionizing healthcare by tailoring treatments to individual patient characteristics. At its core, pharmacogenomics enables the customization of medication prescriptions based on genetic profiles, enhancing drug efficacy and safety. This precision medicine approach addresses disease diagnosis, prevention, and treatment, offering targeted therapies for conditions like autoimmune disorders, rheumatoid arthritis, and neoplastic conditions. Examples of pharmacogenomics and personalized medicine include treatment for certain conditions like blood clotting disorders (warfarin (blood thinner), genetic variability, acute lymphoblastic leukemia (ALL), and thiopurine methyltransferase (TPMT) testing) in leukemia treatment. Historically, personalized medicine has evolved from Hippocrates' humoral theories to modern molecular diagnostics. The shift from cellular to molecular-level investigations has led to the current post-genomic era, emphasizing the four chemical components of DNA in understanding and treating disorders. This evolution enhances our ability to predict disease susceptibility, treatment response, and potential adverse reactions, demanding advancements in privacy laws, payment systems, regulatory standards, and education. Personalized healthcare optimizes treatment by considering genetic, environmental, and lifestyle factors, reducing adverse reactions, and improving patient satisfaction. It drives genomic and biotechnological research, fostering the development of targeted therapies and diagnostic tools, and streamlining drug development. Applications in lung cancer, renal carcinoma, and rheumatoid arthritis (RA) illustrate the efficacy of personalized medicine. Targeted therapies, such as tyrosine kinase inhibitors (TKIs) and monoclonal antibodies (mAbs), show significant success in lung cancer. Biomarkers guide renal carcinoma treatments, while gene expression profiles predict rheumatoid arthritis outcomes with tumor necrosis factor-alpha (TNF-α) blockers. The US FDA's increasing approvals of personalized treatments underscore its commitment to precision medicine. However, regulatory challenges persist, necessitating clear processes and policies. Ethical considerations, including equitable access and privacy, are critical for responsible implementation. The FDA's increasing approvals of personalized treatments highlight its commitment to advancing precision medicine, yet regulatory challenges remain, requiring the development of clear processes and policies to manage innovation safely. While these hurdles are being addressed through evolving guidelines and collaborative efforts, ethical considerations, including equitable access and privacy, are critical for responsible implementation. Early examples, such as warfarin and imatinib, demonstrate the role of genetic information in guiding treatment, illustrating the potential of personalized medicine to optimize healthcare outcomes.

Precision Cancer Medicine 2.0-Oncology in the postgenomic era.

Genomic medicine has transformed the lives of patients with cancer byenabling individualised and evidence-based clinical decision-making. Despite this progress, the implementation of precision cancer medicine is limited by its dependence on isolated biomarkers. The development of bulk and single-cell multiomic technologies has revealed the enormous complexity of the cancer ecosystem. Beyond the cancer cell, the tumour microenvironment, macroenvironment and host factors, including the microbiome, profoundly influence the cancer phenotype, and accounting for these enhances the resolution of precision medicine. The advent of robust multiomic profiling and interpretable machine learning algorithms mark the dawn of a new postgenomic era of personalised cancer medicine. In Precision Cancer Medicine 2.0, high-resolution personalised clinical decision-making is informed by the comprehensive multiomic profiling of tumour and host, integrated using artificial intelligence.

Triumphs and Challenges of Natural Product Discovery in the Postgenomic Era.

Natural products have played significant roles as medicine and food throughout human history. Here, we first provide a brief historical overview of natural products, their classification and biosynthetic origins, and the microbiological and genetic methods used for their discovery. We also describe and discuss the technologies that revolutionized the field, which transitioned from classic genetics to genome-centric discovery approximately two decades ago. We then highlight the most recent advancements and approaches in the current postgenomic era, in which genome mining is a standard operation and high-throughput analytical methods allow parallel discovery of genes and molecules at an unprecedented pace. Finally, we discuss the new challenges faced by the field of natural products and the future of systematic heterologous expression and strain-independent discovery, which promises to deliver more molecules in vials than ever before.

'Wild Type'.

In this opinion piece, we consider the meaning of the term 'wild type' in the context of microbiology. This is especially pertinent in the post-genomic era, where we have a greater awareness of species diversity than ever before. Genomic heterogeneity, in vitro evolution/selection pressures, definition of 'the wild', the size and importance of the pan-genome, gene-gene interactions (epistasis), and the nature of the 'wild-type gene' are all discussed. We conclude that wild type is an outdated and even misleading phrase that should be gradually phased out.

Revisiting Epigenetics Fundamentals and Its Biomedical Implications.

In light of the post-genomic era, epigenetics brings about an opportunity to better understand how the molecular machinery works and is led by a complex dynamic set of mechanisms, often intricate and complementary in many aspects. In particular, epigenetics links developmental biology and genetics, as well as many other areas of knowledge. The present work highlights substantial scopes and relevant discoveries related to the development of the term from its first notions. To our understanding, the concept of epigenetics needs to be revisited, as it is one of the most relevant and multifaceted terms in human knowledge. To redirect future novel experimental or theoretical efforts, it is crucial to compile all significant issues that could impact human and ecological benefit in the most precise and accurate manner. In this paper, the reader can find one of the widest compilations of the landmarks and epistemic considerations of the knowledge of epigenetics across the history of biology from the earliest epigenetic formulation to genetic determinism until the present. In the present work, we link the current body of knowledge and earlier pre-genomic concepts in order to propose a new definition of epigenetics that is faithful to its regulatory nature.

Systemic Therapy in Breast Cancer.

Therapeutic advances in breast cancer have significantly improved outcomes in recent decades. In the early setting, there has been a gradual shift from adjuvant-only to neoadjuvant strategies, with a growing focus on customizing post-neoadjuvant treatments through escalation and de-escalation based on pathologic response. At the same time, the transition from a pre-genomic to a post-genomic era, utilizing specific assays in the adjuvant setting and targeted sequencing in the advanced stage, has deepened our understanding of disease biology and aided in identifying molecular markers associated with treatment benefit. Finally, the introduction of new drug classes such as antibody-drug conjugates, and the incorporation in the (neo)adjuvant setting of therapies previously investigated in the advanced stage, like immunotherapy and CDK4-6 inhibitors, poses new challenges in treatment sequencing.

Emerging roles of long non-coding RNAs in human epilepsy.

Genome-scale biological studies conducted in the post-genomic era have revealed that two-thirds of human genes do not encode proteins. Most functional non-coding RNA transcripts in humans are products of long non-coding RNA (lncRNA) genes, an abundant but still poorly understood class of human genes. As a result of their fundamental and multitasking regulatory roles, lncRNAs are associated with a wide range of human diseases, including neurological disorders. Approximately 40% of lncRNAs are specifically expressed in the brain, and many of them exhibit distinct spatiotemporal patterns of expression. Comparative genomics approaches have determined that 65%-75% of human lncRNA genes are primate-specific and hence can be posited as a contributing potential cause of the higher-order complexity of primates, including human, brains relative to those of other mammals. Although lncRNAs present important mechanistic examples of epileptogenic functions, the human/primate specificity of lncRNAs questions their relevance in rodent models. Here, we present an in-depth review that supports the contention that human lncRNAs are direct contributors to the etiology and pathogenesis of human epilepsy, as a means to accelerate the integration of lncRNAs into clinical practice as potential diagnostic biomarkers and therapeutic targets. Meta-analytically, the major finding of our review is the commonality of lncRNAs in epilepsy and cancer pathogenesis through mitogen-activated protein kinase (MAPK)-related pathways. In addition, neuroinflammation may be a relevant part of the common pathophysiology of cancer and epilepsy. LncRNAs affect neuroinflammation-related signaling pathways such as nuclear factor kappa- light- chain- enhancer of activated B cells (NF-κB), Notch, and phosphatidylinositol 3- kinase/ protein kinase B (Akt) (PI3K/AKT), with the NF-κB pathway being the most common. Besides the controversy over lncRNA research in non-primate models, whether neuroinflammation is triggered by injury and/or central nervous system (CNS) toxicity during epilepsy modeling in animals or is a direct consequence of epilepsy pathophysiology needs to be considered meticulously in future studies.

Disentanglement of Evolutionary Constraints in Statistical Models of Proteins

The exponential growth of protein sequences in the post-genomic era has revolutionized the application of generative sequence models for pivotal tasks such as contact prediction, protein design, alignment, and homology search. Despite remarkable progress in these areas, the interpretability of the modeled pairwise parameters remains limited due to complexities arising from coevolution, phylogeny, and entropy. While post-correction methods for contact prediction have been developed to eliminate entropy-related contributions from predicted contact maps, there is currently no direct approach to correct entropy in other applications reliant on raw parameters. In this paper, we investigate the sources of entropy signal and propose a novel spectral regularizer, LH (an abbreviation of Henri Lebesgue), to mitigate its impact during model fitting. By incorporating this regularizer into the GREMLIN framework (utilizing a Markov random field or Potts model), we enable the accurate inference of sparse contact maps while simultaneously improving interpretability and addressing overfitting concerns critical for sequence evaluation and design. To validate the efficacy of our approach, we design multiple protein sequences based on GREMLIN with both L2 and LH regularizers, and subsequently experimentally measure their using cDNA display proteolysis. Our findings demonstrate that proteins designed using the LH regularizer exhibit increased diversity and enhanced folding stability. Published by the American Physical Society 2024

Proteomics for the identification of peripheral markers in pituitary disease.

Although precision medicine moved its first steps from genomic medicine, it has gone far beyond genomics, considering the full complexity of cellular physiology. Therefore, the present time might be considered as the "post-genomic era." In detail, proteomics captures the overall protein profile of an analyzed sample. The goals of proteomic analysis are to perform a global analysis of protein expression and function, to systematically define the role proteins in physiological and pathological condition, to increase mechanistic understanding of the biological processes and to discover new biomarkers and therapeutic targets. In this narrative mini-review, the role of proteomics is discussed with a particular focus on the few attempts of the application of proteomic platforms for the identification of new biomarkers in pituitary diseases, namely in acromegaly, GH deficiency and male secondary hypogonadism.

Actinomycetes - The Repertoire of Diverse Bioactive Chemical Molecules: From Structures to Antibiotics.

The urgent need for novel antibiotics in the face of escalating global antimicrobial resistance necessitates innovative approaches to identify bioactive compounds. Actinomycetes, renowned for their prolific production of antimicrobial agents, stand as a cornerstone in this pursuit. Their diverse metabolites exhibit multifaceted bioactivities, including potent antituberculosis, anticancer, immunomodulatory, immuno-protective, antidiabetic, etc. Though terrestrial sources have been exploited significantly, contemporary developments in the field of antimicrobial drug discovery have put marine actinomycetes in a prominent light as a promising and relatively unexplored source of novel bioactive molecules. This is further boosted by post-genomic era advances like bioinformatics-based secretome analysis and reverse engineering that have totally revitalized actinomycetes antibiotic research. This review highlights actinomycetes-based chemically diverse scaffolds and clinically validated antibiotics along with the enduring significance of actinomycetes from untouched ecosystems, especially with recent advanced techniques in the quest for next-generation antimicrobials.

Studies on the association of genes with language disorders in the post-genomic era

Since genetic research entered the post-genomic era, the high heritability of language disorders has been confirmed. A variety of genetic-related diseases may cause various types of language disorders in children and/or adults. This article has summarized language disorders and their underlying mechanisms by searching the Web of Science database over the last decade, and combed the genetic researches for dyslexia, frontotemporal degeneration, specific language disorder, childhood speech apraxia and other single diseases that are strongly associated with the language disorders. It also provided a discussion over the co-occurrence of multiple diseases, with an aim to revealing the genetic association and/or pathogenetic mechanism in order to provide inspiration for the prevention, diagnosis, and treatment of language disorders.

Forgetting how we ate: personalised nutrition and the strategic uses of history

Personalised nutrition (PN) has emerged over the past twenty years as a promising area of research in the postgenomic era and has been popularized as the new big thing out of molecular biology. Advocates of PN claim that previous approaches to nutrition sought general and universal guidance that applied to all people. In contrast, they contend that PN operates with the principle that “one size does not fit all” when it comes to dietary guidance. While the molecular mechanisms studied within PN are new, the notion of a personal dietary regime guided by medical advice has a much longer history that can be traced back to Galen’s “On Food and Diet” or Ibn Sina’s (westernized as Avicenna) “Canon of Medicine”. Yet this history is either wholly ignored or misleadingly appropriated by PN proponents. This (mis)use of history, we argue helps to sustain the hype of the novelty of the proposed field and potential commodification of molecular advice that undermines longer histories of food management in premodern and non-Western cultures. Moreover, it elides how the longer history of nutritional advice always happened in a heavily moralized, gendered, and racialized context deeply entwined with collective technologies of power, not just individual advice. This article aims at offering a wider appreciation of this longer history to nuance the hype and exceptionalism surrounding contemporary claims.

G-ACP: a machine learning approach to the prediction of therapeutic peptides for gastric cancer

Conventional Gastrointestinal (GI) cancer treatments are quite expensive and have major hazards. Nowadays, a different strategy places more emphasis on creating tiny biologically active peptides that do not cause severe poisoning. Anticancer peptides (ACPs) are found through experimental screening, which is time-dependent and frequently fraught with difficulties. Gastric ACPs are emerging as a promising GI cancer treatment in the current day. It is crucial to identify novel gastric ACPs to have an improved knowledge of their functioning processes and treatment of gastric cancer. As a result of the post-genomic era’s massive production of peptide sequences, rapid and effective ACPs using a computational method are essential. Several adaptive statistical techniques for distinguishing ACPs and non-ACPs have recently been developed. A variety of adapted statistically significant methods have been developed to differentiate between ACPs and non-ACPs. Despite significant progress, there is no specific model for the prediction of gastric ACPs because the specific model will predict a particular type of peptide more accurately and quickly. To overcome this, an initiative is taken for the creation of a reliable framework for the accurate identification of gastric ACPs. The current technique in particular contains four possible features along with one hybrid feature encoding mechanisms which are the target-class motif previously indicated by Amino Acid Composition, Dipeptide Composition, Tripeptide Composition (TPC), Pseudo Amino Acid Composition (PAAC), and their Hybrid. Machine Learning algorithms make high-performance and accurate prediction tools. Moreover, highly variable and ideal deep feature selection is done using an ANOVA-based F score for feature pruning. Experiments on a range of algorithms are carried out to identify the optimal operating strategy due to the diverse nature of learning. Following analysis of the empirical results, Naïve Bayes with TPC and Hybrid feature space outperforms other methods with 0.99 accuracy score on the testing dataset. To find the model generalization an external validation is carried out. In external datasets, the Extra Trees with PAAC features outperforms with the accuracy of 0.94. The comparison study shows that our suggested model will predict gastric ACPs more accurately and will be useful in drug development and gastric cancer. The predictive model can be freely accessed at https://github.com/humeraazad10/G-ACP.git. Communicated by Ramaswamy H. Sarma

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.