Identify Drug Targets Research Articles

Drug target genes and molecular mechanism investigation in isoflurane-induced anesthesia based on WGCNA and machine learning methods

PurposeThis study sought to identify drug target genes and their associated molecular mechanisms during isoflurane-induced anesthesia in clinical applications.MethodsMicroarray data (ID: GSE64617; isoflurane-treated vs. normal samples) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened and hub genes were investigated using weighted correlation network analysis (WGCNA). Protein–protein interactions (PPIs) were constructed among the co-DEGs (common genes between DEGs and hub genes), followed by functional enrichment analyses. Then, three machine learning methods were used to reveal drug targets, followed by validation, nomogram analysis, and gene set enrichment analysis. Finally, an miRNA-target network was constructed.ResultsA total of 686 DEGs were identified between the two groups—of which, 183 DEGs integrated with genes revealed by WCGNA were identified as co-genes. These genes, including contactin-associated protein 1 (CNTNAP1), are mainly involved in functions such as action potentials. PPI network analysis revealed three models, with the machine learning analysis exploring four drug target genes: A2H, FAM155B, SCARF2, and SDR16C5. ROC and nomogram analyses demonstrated the ideal diagnostic value of these target genes. Finally, miRNA–mRNA pairs were constructed based on the four mRNAs and associated 174 miRNAs.ConclusionFA2H, FAM155B, SCARF2, and SDR16C5 may be novel drug target genes for isoflurane-induced anesthesia. CNTNAP1 may participate in the progression of isoflurane-induced anesthesia via its action potential function.

Pangenome diversification and resistance gene characterization in Salmonella Typhi prioritized RfaJ as a significant therapeutic marker

BackgroundSalmonella Typhi stands as the etiological agent responsible for the onset of human typhoid fever. The pressing demand for innovative therapeutic targets against S. Typhi is underscored by the escalating prevalence of this pathogen and the severe nature of its infections. Consequently, this study employs pangenome analysis to scrutinize 119 S. Typhi-resistant strains, aiming to identify the most promising therapeutic targets originating from its core genome. ResultsSubtractive genomics was employed to systematically eliminate non-homologous (n=1147), essential (n=551), drug-like (n=80), and pathogenicity-related (n=18) proteins from the initial pool of 3351 core genome proteins. Consequently, lipopolysaccharide 1,2-glucosyltransferase RfaJ was designated as the optimal pharmacological target due to its potential versatility. Furthermore, a compendium of 9000 FDA-approved compounds was repurposed for evaluation against the RfaJ drug target, with the specific intent of prioritizing novel, high-potency therapeutic candidates for combating S. Typhi. Ultimately, four compounds, namely DB00549 (Zafirlukast), DB15637 (Fluzoparib), DB15688 (Zavegepant), and DB12411 (Bemcentinib), were singled out as potential inhibitors based on the ligand-protein binding affinity (indicated by the lowest anticipated binding energy) and the overall stability of these compounds. Notably, molecular dynamics simulations, conducted over a 50 nanosecond interval, convincingly demonstrated the stability of these compounds in the context of the RfaJ protein. ConclusionIn summary, the present findings hold significant promise as an initial stride in the broader drug discovery endeavor against S. Typhi infections. However, the experimental validation of the identified drug target and drug candidate is further required to increase the effectiveness of the applied methodology.

Identifying drug targets with thermal proteome profiling using IBT‐16plex

RationaleThermal proteome profiling (TPP) has been widely used for the identification of drug targets for several years, and TMTpro‐16plex has recently been evaluated for TPP of vehicle‐ and drug‐treated samples in a single labeling process to reduce missing values and save instrument time. A novel isobaric labeling reagent, IBT‐16plex, was developed with slightly better performance in protein identification and quantification than the commercially available TMTpro‐16plex.MethodsIn this study, we applied the newly developed IBT‐16plex for target identification of methotrexate and panobinostat using TPP.ResultsThe known targets of these two drugs were successfully identified with elevated melting temperatures, and some known off‐targets and potential new off‐targets were also identified.ConclusionsIBT‐16plex can be a cost‐effective replacement for TMTpro‐16plex for TPP applications.

Network Based Gene Interaction Analysis of antiviral Proteins Associated with Interferon Mediated JAK-STAT Signaling Pathway

Recent increase in disease gene database resources resulted in surplus data about immune and inflammatory genes involved in host-viral interactions. In the present study, we used most prominent human immune system related genes namely STAT1, STAT2, TYK2 and OAS1 to understand their network of functional importance in identifying drug targets through protein protein interactions (PPIs) and functional enrichment analysis using gene ontology (GO) databases like Stringbase, Genemania, GoNet Dice and DAVID. Our study design involved collection of antiviral genes and selecting functionally enriched gene through protein interaction network analysis. We found 146 to 1689 PPIs, in which 10 genes were repeatedly interacting with high interaction scores. Five genes namely IFNAR1, IFNAR2, PIAS1, IFR9 and JAK1 were commonly present in Stringbase and Genemania. CREBBP, EP300, IFNGR1, IRF1 and JAK2 were additional new genes found in Stringbase and Genemania. A maximum of 60.9% functional fold increment was found with STAT1, STAT2, TYK2 and OAS1 gene clusters in type-1 interferon mediated JAK-STAT signaling pathways associated with viral infections and biotic stimulus. This bioinformatics study involved no ethical committee clearances. Our protein network analysis resulted in identification of functionally important high level host immune response genes, which helps in identification of potential antiviral drug targets and understanding disease susceptible genes in viral infections.

The effect of immunomodulatory drugs on aortic stenosis: a Mendelian randomisation analysis.

There are currently no approved pharmacological treatment options for aortic stenosis (AS), and there are limited identified drug targets for this chronic condition. It remains unclear whether inflammation plays a role in AS pathogenesis and whether immunomodulation could become a therapeutic target. We evaluated the potentially causal association between inflammation and AS by investigating the genetically proxied effects of tocilizumab (IL6 receptor, IL6R, inhibitor), canakinumab (IL1β inhibitor) and colchicine (β-tubulin inhibitor) through a Mendelian randomisation (MR) approach. Genetic proxies for these drugs were identified as single nucleotide polymorphisms (SNPs) in the gene, enhancer or promoter regions of IL6R, IL1β or β-tubulin gene isoforms, respectively, that were significantly associated with serum C-reactive protein (CRP) in a large European genome-wide association study (GWAS; 575,531 participants). These were paired with summary statistics from a large GWAS of AS in European patients (653,867 participants) to then perform primary inverse-variance weighted random effect and sensitivity MR analyses for each exposure. This analysis showed that genetically proxied tocilizumab was associated with reduced risk of AS (OR 0.56, 95% CI 0.45-0.70 per unit decrease in genetically predicted log-transformed CRP). Genetically proxied canakinumab was not associated with risk of AS (OR 0.80, 95% CI 0.51-1.26), and only one suitable SNP was identified to proxy the effect of colchicine (OR 34.37, 95% CI 1.99-592.89). The finding that genetically proxied tocilizumab was associated with reduced risk of AS is concordant with an inflammatory hypothesis of AS pathogenesis. Inhibition of IL6R may be a promising therapeutic target for AS management.

Anti-Viral Activity of Bioactive Molecules of Silymarin against COVID-19 via In Silico Studies.

The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection drove the global coronavirus disease 2019 (COVID-19) pandemic, causing a huge loss of human life and a negative impact on economic development. It is an urgent necessity to explore potential drugs against viruses, such as SARS-CoV-2. Silymarin, a mixture of herb-derived polyphenolic flavonoids extracted from the milk thistle, possesses potent antioxidative, anti-apoptotic, and anti-inflammatory properties. Accumulating research studies have demonstrated the killing activity of silymarin against viruses, such as dengue virus, chikungunya virus, and hepatitis C virus. However, the anti-COVID-19 mechanisms of silymarin remain unclear. In this study, multiple disciplinary approaches and methodologies were applied to evaluate the potential mechanisms of silymarin as an anti-viral agent against SARS-CoV-2 infection. In silico approaches such as molecular docking, network pharmacology, and bioinformatic methods were incorporated to assess the ligand-protein binding properties and analyze the protein-protein interaction network. The DAVID database was used to analyze gene functions, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment. TCMSP and GeneCards were used to identify drug target genes and COVID-19-related genes. Our results revealed that silymarin compounds, such as silybin A/B and silymonin, displayed triplicate functions against SARS-CoV-2 infection, including directly binding with human angiotensin-converting enzyme 2 (ACE2) to inhibit SARS-CoV-2 entry into the host cells, directly binding with viral proteins RdRp and helicase to inhibit viral replication and proliferation, and regulating host immune response to indirectly inhibit viral infection. Specifically, the targets of silymarin molecules in immune regulation were screened out, such as proinflammatory cytokines TNF and IL-6 and cell growth factors VEGFA and EGF. In addition, the molecular mechanism of drug-target protein interaction was investigated, including the binding pockets of drug molecules in human ACE2 and viral proteins, the formation of hydrogen bonds, hydrophobic interactions, and other drug-protein ligand interactions. Finally, the drug-likeness results of candidate molecules passed the criteria for drug screening. Overall, this study demonstrates the molecular mechanism of silymarin molecules against SARS-CoV-2 infection.

STPP-UP: An alternative method for drug target identification using protein thermal stability

Thermal proteome profiling (TPP) has significantly advanced the field of drug discovery by facilitating proteome-wide identification of drug targets and off-targets. However, TPP has not been widely applied for high-throughput drug screenings, since the method is labor intensive and requires a lot of measurement time on a mass spectrometer. Here, we present Single-tube TPP with Uniform Progression (STPP-UP), which significantly reduces both the amount of required input material and measurement time, while retaining the ability to identify drug targets for compounds of interest. By using incremental heating of a single sample, changes in protein thermal stability across a range of temperatures can be assessed, while alleviating the need to measure multiple samples heated to different temperatures. We demonstrate that STPP-UP is able to identify the direct interactors for anticancer drugs in both human and mice cells. In summary, the STPP-UP methodology represents a useful tool to advance drug discovery and drug repurposing efforts.

Death-seq identifies regulators of cell death and senolytic therapies

Selectively ablating damaged cells is an evolving therapeutic approach for age-related disease. Current methods for genome-wide screens to identify genes whose deletion might promote the death of damaged or senescent cells are generally underpowered because of the short timescales of cell death as well as the difficulty of scaling non-dividing cells. Here, we establish "Death-seq," a positive-selection CRISPR screen optimized to identify enhancers and mechanisms of cell death. Our screens identified synergistic enhancers of cell death induced by the known senolytic ABT-263. The screen also identified inducers of cell death and senescent cell clearance in models of age-related diseases by a related compound, ABT-199, which alone is not senolytic but exhibits less toxicity than ABT-263. Death-seq enables the systematic screening of cell death pathways to uncover molecular mechanisms of regulated cell death subroutines and identifies drug targets for the treatment of diverse pathological states such as senescence, cancer, and fibrosis.

Hepatocellular Carcinoma: Past and Present Challenges and Progress in Molecular Classification and Precision Oncology.

Hepatocellular carcinoma (HCC) is one of the most common solid tumor malignancies in the world and represents roughly 90% of all primary malignancies of the liver. The most common risk factors for HCC include hepatitis B virus, hepatitis C virus, alcohol, and increasingly, fatty liver. Most HCC is diagnosed at advanced stages, excluding the possibility of curative resection, which leaves systemic therapy as the only treatment option. However, given the extreme mutational diversity and heterogenous nature of HCC, efforts to develop new targeted systemic therapies were largely unsuccessful until recently. HCC pathogenesis is thought to be a multistage process driven by a wide array of nonmutually exclusive driver mutations accompanied by many passenger mutations, with the average tumor possessing approximately 40 genomic aberrations. Over the past two decades, several efforts to categorize HCC prognostically and therapeutically according to different molecular subclassifications with the intent to guide treatment and identify drug targets have emerged, though, no single consensus has been reached. Recent breakthroughs in drug development have greatly expanded treatment options, but the ideal of uniting each patient's unique HCC with a targeted systemic therapy remains elusive.

Metabolomics in drug research and development: The recent advances in technologies and applications.

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding of metabolism in biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. More importantly, metabolomics greatly accelerates the drug development process by predicting pharmacokinetics, pharmacodynamics, and drug response. In addition, metabolomics facilitates the exploration of drug repurposing and drug-drug interactions, as well as the development of personalized treatment strategies. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

Dehydroevodiamine Targeting ACHE and ALDH3A1 Regulate Glycolytic Metabolism to Inhibit Hepatocellular Carcinoma Cell Apoptosis

Dehydroevodiamine (DHE), a natural quinazoline alkaloid used in Chinese herbal medicine, exhibits promising antitumor properties in various cancers, including gastric cancer. Glycolysis is essential for tumor proliferation and survival. The antitumor effect of DHE on hepatoma cells (hepatocellular carcinoma, HCC) and the mechanism underlying the glycolytic regulation remain unclear. In this study, the cell viability, apoptosis rate, lactate release, and glucose uptake were evaluated in hepatoma cells treated with DHE. Cell metabolites were analyzed using GC-MS, and network pharmacology was employed to identify potential drug targets for DHE. Furthermore, the antiproliferative effect of DHE on liver cancer cells was examined by silencing the identified drug target. DHE promotes the apoptosis of Huh-7 and PLC cells. Cell metabonomics studies have shown that DHE primarily modulates the glycolysis/gluconeogenesis pathway. Simultaneously, DHE can inhibit the key enzymes HK2, PFKFB3, and LDHA of the glycolysis pathway, thus inhibiting glucose uptake and lactic acid production. Network pharmacological analysis identified ACHE and ALDH3A1 as the drug targets of DHE in liver cancer. Silencing ACHE and ALDH3A1 resulted in the loss of the apoptosis-promoting effect of DHE. Thus, DHE sensitizes aerobic glycolytic hepatoma cells to apoptosis by directly downregulating ACHE and ALDH3A1, leading to the inactivation of HK2, PFKFB3, and LDHA and suppression of aerobic glycolysis. DHE effectively inhibits the progression of liver cancer, primarily by reducing glucose metabolism, thereby inhibiting apoptosis of liver cancer cells. Therefore, DHE could be a promising agent for molecular-targeted cancer treatment strategies.

Integrating inflammatory biomarker analysis and artificial-intelligence-enabled image-based profiling to identify drug targets for intestinal fibrosis

Intestinal fibrosis, often caused by inflammatory bowel disease, can lead to intestinal stenosis and obstruction, but there are no approved treatments. Drug discovery has been hindered by the lack of screenable cellular phenotypes. To address this, we used a scalable image-based morphology assay called Cell Painting, augmented with machine learning algorithms, to identify small molecules that could reverse the activated fibrotic phenotype of intestinal myofibroblasts. We then conducted a high-throughput small molecule chemogenomics screen of approximately 5,000 compounds with known targets or mechanisms, which have achieved clinical stage or approval by the FDA. By integrating morphological analyses and AI using pathologically relevant cells and disease-relevant stimuli, we identified several compounds and target classes that are potentially able to treat intestinal fibrosis. This phenotypic screening platform offers significant improvements over conventional methods for identifying a wide range of drug targets.

Genome-wide association analysis and Mendelian randomization proteomics identify drug targets for heart failure

We conduct a large-scale meta-analysis of heart failure genome-wide association studies (GWAS) consisting of over 90,000 heart failure cases and more than 1 million control individuals of European ancestry to uncover novel genetic determinants for heart failure. Using the GWAS results and blood protein quantitative loci, we perform Mendelian randomization and colocalization analyses on human proteins to provide putative causal evidence for the role of druggable proteins in the genesis of heart failure. We identify 39 genome-wide significant heart failure risk variants, of which 18 are previously unreported. Using a combination of Mendelian randomization proteomics and genetic cis-only colocalization analyses, we identify 10 additional putatively causal genes for heart failure. Findings from GWAS and Mendelian randomization-proteomics identify seven (CAMK2D, PRKD1, PRKD3, MAPK3, TNFSF12, APOC3 and NAE1) proteins as potential targets for interventions to be used in primary prevention of heart failure.

Identification of IMP Dehydrogenase as a Potential Target for Anti-Mpox Virus Agents.

Mpox virus (formerly monkeypox virus [MPXV]) is a neglected zoonotic pathogen that caused a worldwide outbreak in May 2022. Given the lack of an established therapy, the development of an anti-MPXV strategy is of vital importance. To identify drug targets for the development of anti-MPXV agents, we screened a chemical library using an MPXV infection cell assay and found that gemcitabine, trifluridine, and mycophenolic acid (MPA) inhibited MPXV propagation. These compounds showed broad-spectrum anti-orthopoxvirus activities and presented lower 90% inhibitory concentrations (0.026 to 0.89 μM) than brincidofovir, an approved anti-smallpox agent. These three compounds have been suggested to target the postentry step to reduce the intracellular production of virions. Knockdown of IMP dehydrogenase (IMPDH), the rate-limiting enzyme of guanosine biosynthesis and a target of MPA, dramatically reduced MPXV DNA production. Moreover, supplementation with guanosine recovered the anti-MPXV effect of MPA, suggesting that IMPDH and its guanosine biosynthetic pathway regulate MPXV replication. By targeting IMPDH, we identified a series of compounds with stronger anti-MPXV activity than MPA. This evidence shows that IMPDH is a potential target for the development of anti-MPXV agents. IMPORTANCE Mpox is a zoonotic disease caused by infection with the mpox virus, and a worldwide outbreak occurred in May 2022. The smallpox vaccine has recently been approved for clinical use against mpox in the United States. Although brincidofovir and tecovirimat are drugs approved for the treatment of smallpox by the U.S. Food and Drug Administration, their efficacy against mpox has not been established. Moreover, these drugs may present negative side effects. Therefore, new anti-mpox virus agents are needed. This study revealed that gemcitabine, trifluridine, and mycophenolic acid inhibited mpox virus propagation and exhibited broad-spectrum anti-orthopoxvirus activities. We also suggested IMP dehydrogenase as a potential target for the development of anti-mpox virus agents. By targeting this molecule, we identified a series of compounds with stronger anti-mpox virus activity than mycophenolic acid.

Rare coding variants in CHRNB2 reduce the likelihood of smoking

Human genetic studies of smoking behavior have been thus far largely limited to common variants. Studying rare coding variants has the potential to identify drug targets. We performed an exome-wide association study of smoking phenotypes in up to 749,459 individuals and discovered a protective association in CHRNB2, encoding the β2 subunit of the α4β2 nicotine acetylcholine receptor. Rare predicted loss-of-function and likely deleterious missense variants in CHRNB2 in aggregate were associated with a 35% decreased odds for smoking heavily (odds ratio (OR) = 0.65, confidence interval (CI) = 0.56–0.76, P = 1.9 × 10−8). An independent common variant association in the protective direction (rs2072659; OR = 0.96; CI = 0.94–0.98; P = 5.3 × 10−6) was also evident, suggesting an allelic series. Our findings in humans align with decades-old experimental observations in mice that β2 loss abolishes nicotine-mediated neuronal responses and attenuates nicotine self-administration. Our genetic discovery will inspire future drug designs targeting CHRNB2 in the brain for the treatment of nicotine addiction.

MOSAIC: Multi-Omic Spatial Atlas in Cancer, effect on precision oncology.

e15076 Background: Precision oncology aims to first break diagnoses into biologically distinct subtypes, then pursue personalized therapies for each group of patients ( Garraway et al, J Clin Onc, 2013). This strategy is enabled by recent advances in technologies for medical imaging, molecular profiling, and artificial intelligence. Spatially resolved molecular profiling (“spatial omics”) is an emerging technology that harnesses all three of these advances ( Navarro et al, Science, 2016; Rao et al, Nature, 2021). Cancer is driven by localized interactions between tumor cells, immune and non-immune stromal components. Methods: Spatial omics studies have revealed specific cell types and pathways that control those tumor-microenvironment crosstalks ( Hunter et al, Nat Comm, 2021; Alon et al, Science, 2021), involving both innate and adaptive immunity ( Binnewies et al, Nat Med, 2018). Identifying new cancer subtypes based on the spatially localized tumor-host interactions across many patients have the potential to revolutionize immuno-oncology. The advances in utilizing AI-based methods have so far been unattainable due to a small number of patients in spatial omics studies. Results: Here we present the MOSAIC project - a collaborative initiative across industry and top oncology hospitals to build the largest collection of spatial omics data in cancer. We combine comprehensive clinical annotation with new deep profiling methods to both discover cancer subtypes and identify drug targets and biomarkers within them. MOSAIC aims to generate multimodal data for a total of 7,000 patients in seven cancer indications. Data modalities will include spatial and single cell transcriptomics and proteomics, bulk molecular profiling, pathology images, and curated clinical information. This collaboration harnesses the strengths of academia and industry to provide patient samples, generate high-quality data, develop AI-based analytical tools, and compile a resource that will eventually be made widely available for medical research. Conclusions: Here we present the workflow of the MOSAIC study, demonstrate the data processing pipelines and show spatial distributions of transcripts within the tumor microenvironment. Building upon these initial datasets, we discuss how the MOSAIC parties will collaborate to build an unprecedented database for research and discovery of new immuno-oncology therapeutic approaches.

Inborn Errors of Immunity in Hidradenitis Suppurativa Pathogenesis and Disease Burden.

Hidradenitis suppurativa (HS), also known as Verneuil's disease and acne inversa, is a prevalent, debilitating, and understudied inflammatory skin disease. It is marked by repeated bouts of pathological inflammation causing pain, hyperplasia, aberrant healing, and fibrosis. HS is difficult to manage and has many unmet medical needs. There is clinical and pharmacological evidence for extensive etiological heterogeneity with HS, suggesting that this clinical diagnosis is capturing a spectrum of disease entities. Human genetic studies provide robust insight into disease pathogenesis. They also can be used to resolve etiological heterogeneity and to identify drug targets. However, HS has not been extensively investigated with well-powered genetic studies. Here, we review what is known about its genetic architecture. We identify overlap in molecular, cellular, and clinical features between HS and inborn errors of immunity (IEI). This evidence indicates that HS may be an underrecognized component of IEI and suggests that undiagnosed IEI are present in HS cohorts. Inborn errors of immunity represent a salient opportunity for rapidly resolving the immunological landscape of HS pathogenesis, for prioritizing drug repurposing studies, and for improving the clinical management of HS.

A computational pipeline for drug discovery in immunometabolism against autoimmune diseases

Abstract We developed a computational pipeline to study immunometabolism for drug discovery in immune mediated diseases. Our pipeline integrates RNA-seq and proteomics data to build genome scale metabolic models and integrate these models with external drug databases and patient data to perform metabolic flux analyses for identifying drug targets and repurposing. We applied our pipeline to explore the metabolism of B cells in the context of rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). We constructed metabolic models of B cells, performed enzymatic inhibition of targets of existing drugs and compounds, and integrated the model's results with differentially expressed genes in RA and SLE patients. Using a computational scoring method, we ranked the inhibited targets based on their ability to stimulate the reactions of differentially expressed genes in diseases to have metabolic flux in opposite directions of their up and down-regulation. As a result, we identified 25 B cell drug targets against RA and 23 B cell drug targets against SLE. We validated these targets by comparing them against the current treatment options for these diseases. For RA, five of the identified targets are genes that are already targeted by existing drugs used to treat RA. Our top-ranked targets for RA are adenine phosphoribosyltransferase, galactosidase beta 1, and aldehyde dehydrogenase 5 family member A1. For SLE, the top-ranked new targets are geranylgeranyl diphosphate synthase 1, glutathione-disulfide reductase, and galactosidase beta 1. Our pipeline provides a streamlined approach for identifying drug targets and predicting repurposable drugs and can facilitate the investigation of novel indications for diseases to improve global health. Supported by Layman Seed grant from University of Nebraska Foundation, and NIH grant R35GM119770.

Memory Unlocks the Future of Biomolecular Dynamics: Transformative Tools to Uncover Physical Insights Accurately and Efficiently.

Conformational changes underpin function and encode complex biomolecular mechanisms. Gaining atomic-level detail of how such changes occur has the potential to reveal these mechanisms and is of critical importance in identifying drug targets, facilitating rational drug design, and enabling bioengineering applications. While the past two decades have brought Markov state model techniques to the point where practitioners can regularly use them to glimpse the long-time dynamics of slow conformations in complex systems, many systems are still beyond their reach. In this Perspective, we discuss how including memory (i.e., non-Markovian effects) can reduce the computational cost to predict the long-time dynamics in these complex systems by orders of magnitude and with greater accuracy and resolution than state-of-the-art Markov state models. We illustrate how memory lies at the heart of successful and promising techniques, ranging from the Fokker-Planck and generalized Langevin equations to deep-learning recurrent neural networks and generalized master equations. We delineate how these techniques work, identify insights that they can offer in biomolecular systems, and discuss their advantages and disadvantages in practical settings. We show how generalized master equations can enable the investigation of, for example, the gate-opening process in RNA polymerase II and demonstrate how our recent advances tame the deleterious influence of statistical underconvergence of the molecular dynamics simulations used to parameterize these techniques. This represents a significant leap forward that will enable our memory-based techniques to interrogate systems that are currently beyond the reach of even the best Markov state models. We conclude by discussing some current challenges and future prospects for how exploiting memory will open the door to many exciting opportunities.

Genome-Wide Libraries for Protozoan Pathogen Drug Target Screening Using Yeast Surface Display.

The lack of genetic tools to manipulate protozoan pathogens has limited the use of genome-wide approaches to identify drug or vaccine targets and understand these organisms' biology. We have developed an efficient method to construct genome-wide libraries for yeast surface display (YSD) and developed a YSD fitness screen (YSD-FS) to identify drug targets. We show the efficacy of our method by generating genome-wide libraries for Trypanosoma brucei, Trypanosoma cruzi, and Giardia lamblia parasites. Each library has a diversity of ∼105 to 106 clones, representing ∼6- to 30-fold of the parasite's genome. Nanopore sequencing confirmed the libraries' genome coverage with multiple clones for each parasite gene. Western blot and imaging analysis confirmed surface expression of the G. lamblia library proteins in yeast. Using the YSD-FS assay, we identified bonafide interactors of metronidazole, a drug used to treat protozoan and bacterial infections. We also found enrichment in nucleotide-binding domain sequences associated with yeast increased fitness to metronidazole, indicating that this drug might target multiple enzymes containing nucleotide-binding domains. The libraries are valuable biological resources for discovering drug or vaccine targets, ligand receptors, protein-protein interactions, and pathogen-host interactions. The library assembly approach can be applied to other organisms or expression systems, and the YSD-FS assay might help identify new drug targets in protozoan pathogens.