A guide to selecting high-performing antibodies for Huntingtin (UniProt ID: P42858) for use in western blot, immunoprecipitation, and immunofluorescence.

Huntingtin encodes a 3144 amino acid protein, with a polyglutamine repeat tract at the N-terminus. Expansion of this repeat tract above a pathogenic threshold of 36 repeats is the causative mutation of Huntington's disease, a neurodegenerative disorder characterized by loss of striatal neurons. Here we have characterized twenty Huntingtin commercial antibodies for western blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. These studies are part of a larger, collaborative initiative seeking to address antibody reproducibility issues by characterizing commercially available antibodies for human proteins and publishing the results openly as a resource for the scientific community. While use of antibodies and protocols vary between laboratories, we encourage readers to use this report as a guide to select the most appropriate antibodies for their specific needs.

A data science roadmap for open science organizations engaged in early-stage drug discovery.

The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery. We present here the recommendations of a working group composed of experts from both the public and private sectors. Robust data management requires precise ontologies and standardized vocabulary while a centralized database architecture across laboratories facilitates data integration into high-value datasets. Lab automation and opening electronic lab notebooks to data mining push the boundaries of data sharing and data modeling. Important considerations for building robust machine-learning models include transparent and reproducible data processing, choosing the most relevant data representation, defining the right training and test sets, and estimating prediction uncertainty. Beyond data-sharing, cloud-based computing can be harnessed to build and disseminate machine-learning models. Important vectors of acceleration for hit and chemical probe discovery will be (1) the real-time integration of experimental data generation and modeling workflows within design-make-test-analyze (DMTA) cycles openly, and at scale and (2) the adoption of a mindset where data scientists and experimentalists work as a unified team, and where data science is incorporated into the experimental design.

Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications.

Antibodies are critical reagents to detect and characterize proteins. It is commonly understood that many commercial antibodies do not recognize their intended targets, but information on the scope of the problem remains largely anecdotal, and as such, feasibility of the goal of at least one potent and specific antibody targeting each protein in a proteome cannot be assessed. Focusing on antibodies for human proteins, we have scaled a standardized characterization approach using parental and knockout cell lines (Laflamme et al., 2019) to assess the performance of 614 commercial antibodies for 65 neuroscience-related proteins. Side-by-side comparisons of all antibodies against each target, obtained from multiple commercial partners, have demonstrated that: (i) more than 50% of all antibodies failed in one or more applications, (ii) yet, ~50-75% of the protein set was covered by at least one high-performing antibody, depending on application, suggesting that coverage of human proteins by commercial antibodies is significant; and (iii) recombinant antibodies performed better than monoclonal or polyclonal antibodies. The hundreds of underperforming antibodies identified in this study were found to have been used in a large number of published articles, which should raise alarm. Encouragingly, more than half of the underperforming commercial antibodies were reassessed by the manufacturers, and many had alterations to their recommended usage or were removed from the market. This first study helps demonstrate the scale of the antibody specificity problem but also suggests an efficient strategy toward achieving coverage of the human proteome; mine the existing commercial antibody repertoire, and use the data to focus new renewable antibody generation efforts.

Commercially produced antibodies are essential research tools. Investigators at universities and pharmaceutical companies use them to study human proteins, which carry out all the functions of the cells. Scientists usually buy antibodies from commercial manufacturers who produce more than 6 million antibody products altogether. Yet many commercial antibodies do not work as advertised. They do not recognize their intended protein target or may flag untargeted proteins. Both can skew research results and make it challenging to reproduce scientific studies, which is vital to scientific integrity. Using ineffective commercial antibodies likely wastes $1 billion in research funding each year. Large-scale validation of commercial antibodies by an independent third party could reduce the waste and misinformation associated with using ineffective commercial antibodies. Previous research testing an antibody validation pipeline showed that a commercial antibody widely used in studies to detect a protein involved in amyotrophic lateral sclerosis did not work. Meanwhile, the best-performing commercial antibodies were not used in research. Testing commercial antibodies and making the resulting data available would help scientists identify the best study tools and improve research reliability. Ayoubi et al. collaborated with antibody manufacturers and organizations that produce genetic knock-out cell lines to develop a system validating the effectiveness of commercial antibodies. In the experiments, Ayoubi et al. tested 614 commercial antibodies intended to detect 65 proteins involved in neurologic diseases. An effective antibody was available for about two thirds of the 65 proteins. Yet, hundreds of the antibodies, including many used widely in studies, were ineffective. Manufacturers removed some underperforming antibodies from the market or altered their recommended uses based on these data. Ayoubi et al. shared the resulting data on Zenodo, a publicly available preprint database. The experiments suggest that 20-30% of protein studies use ineffective antibodies, indicating a substantial need for independent assessment of commercial antibodies. Ayoubi et al. demonstrated their side-by-side antibody comparison methods were an effective and efficient way of validating commercial antibodies. Using this approach to test commercial antibodies against all human proteins would cost about $50 million. But it could save much of the $1 billion wasted each year on research involving ineffective antibodies. Independent validation of commercial antibodies could also reduce wasted efforts by scientists using ineffective antibodies and improve the reliability of research results. It would also enable faster, more reliable research that may help scientists understand diseases and develop new therapies to improve patient's lives.

Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications.

Antibodies are critical reagents to detect and characterize proteins. It is commonly understood that many commercial antibodies do not recognize their intended targets, but information on the scope of the problem remains largely anecdotal, and as such, feasibility of the goal of at least one potent and specific antibody targeting each protein in a proteome cannot be assessed. Focusing on antibodies for human proteins, we have scaled a standardized characterization approach using parental and knockout cell lines (Laflamme et al., 2019) to assess the performance of 614 commercial antibodies for 65 neuroscience-related proteins. Side-by-side comparisons of all antibodies against each target, obtained from multiple commercial partners, demonstrates that: i) more than 50% of all antibodies failed in one or more tests, ii) yet, ~50-75% of the protein set was covered by at least one high-performing antibody, depending on application, suggesting that coverage of human proteins by commercial antibodies is significant; and iii) recombinant antibodies performed better than monoclonal or polyclonal antibodies. The hundreds of underperforming antibodies identified in this study were found to have been used in a large number of published articles, which should raise alarm. Encouragingly, more than half of the underperforming commercial antibodies were reassessed by the manufacturers, and many had alterations to their recommended usage or were removed from the market. This first such study helps demonstrate the scale of the antibody specificity problem but also suggests an efficient strategy toward achieving coverage of the human proteome; mine the existing commercial antibody repertoire, and use the data to focus new renewable antibody generation efforts.

Target 2035 - an update on private sector contributions.

Target 2035, an international federation of biomedical scientists from the public and private sectors, is leveraging 'open' principles to develop a pharmacological tool for every human protein. These tools are important reagents for scientists studying human health and disease and will facilitate the development of new medicines. It is therefore not surprising that pharmaceutical companies are joining Target 2035, contributing both knowledge and reagents to study novel proteins. Here, we present a brief progress update on Target 2035 and highlight some of industry's contributions.

Open drug discovery in Alzheimer's disease.

Alzheimer's disease (AD) drug discovery has focused on a set of highly studied therapeutic hypotheses, with limited success. The heterogeneous nature of AD processes suggests that a more diverse, systems-integrated strategy may identify new therapeutic hypotheses. Although many target hypotheses have arisen from systems-level modeling of human disease, in practice and for many reasons, it has proven challenging to translate them into drug discovery pipelines. First, many hypotheses implicate protein targets and/or biological mechanisms that are under-studied, meaning there is a paucity of evidence to inform experimental strategies as well as high-quality reagents to perform them. Second, systems-level targets are predicted to act in concert, requiring adaptations in how we characterize new drug targets. Here we posit that the development and open distribution of high-quality experimental reagents and informatic outputs-termed target enabling packages (TEPs)-will catalyze rapid evaluation of emerging systems-integrated targets in AD by enabling parallel, independent, and unencumbered research.

Identification of highly specific antibodies for Serine/threonine-protein kinase TBK1 for use in immunoblot, immunoprecipitation and immunofluorescence.

TBK1 is a serine-threonine protein kinase that has been linked to a number of diseases including amyotrophic lateral sclerosis and frontotemporal dementia. Reproducible research on TBK1 has been hampered by the lack of well characterized antibodies. In this study, we characterized 11 commercial antibodies for TBK1 for use in immunoblot, immunofluorescence and immunoprecipitation, using an isogeneic knock-out cell line as a control. We identify antibodies that appear specific for all three applications but invite the readers to interpret the present findings based on their own scientific expertise and use this report as a guide to select the most appropriate antibody for their specific needs.

An open source plant kinase chemogenomics set.

One hundred twenty-nine protein kinases, selected to represent the diversity of the rice (Oryza sativa) kinome, were cloned and tested for expression in Escherichia coli. Forty of these rice kinases were purified and screened using differential scanning fluorimetry (DSF) against 627 diverse kinase inhibitors, with a range of structures and activities targeting diverse human kinases. Thirty-seven active compounds were then tested for their ability to modify primary root development in Arabidopsis. Of these, 14 compounds caused a significant reduction of primary root length compared with control plants. Two of these inhibitory compounds bind to the predicted orthologue of Arabidopsis PSKR1, one of two receptors for PSK, a small sulfated peptide that positively controls root development. The reduced root length phenotype could not be rescued by the exogenous addition of the PSK peptide, suggesting that chemical treatment may inhibit both PSKR1 and its closely related receptor PSKR2. Six of the compounds acting as root growth inhibitors in Arabidopsis conferred the same effect in rice. Compound RAF265 (CHIR-265), previously shown to bind the human kinase BRAF (B-Raf proto-oncogene, serine/threonine kinase), also binds to nine highly conserved rice kinases tested. The binding of human and rice kinases to the same compound suggests that human kinase inhibitor sets will be useful for dissecting the function of plant kinases.

A Target Engagement Assay to Assess Uptake, Potency, and Retention of Antibiotics in Living Bacteria.

New antibiotics are urgently needed to counter the emergence of antimicrobial-resistant pathogenic bacteria. A major challenge in antibiotic drug discovery is to turn potent biochemical inhibitors of essential bacterial components into effective antimicrobials. This difficulty is underpinned by a lack of methods to investigate the physicochemical properties needed for candidate antibiotics to permeate the bacterial cell envelope and avoid clearance by the action of bacterial efflux pumps. To address these issues, here we used a target engagement assay to measure the equilibrium and kinetic binding parameters of antibiotics targeting dihydrofolate reductase (DHFR) in live bacteria. We also used this assay to identify novel DHFR ligands having antimicrobial activity. We validated this approach using the Gram-negative bacteria Escherichia coli and the emerging human pathogen Mycobacterium abscessus. We expect the use of target engagement assays in bacteria to expedite the discovery and progression of novel, cell-permeable antibiotics with on-target activity.

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.

Stopping pandemics before they start: Lessons learned from SARS-CoV-2.

The vaccine and drug discovery responses to COVID-19 have worked far better than could have been imagined. Yet by the end of 2021, more than 5 million people had died, and the pandemic continues to evolve and rage globally. This Review will describe how each of the vaccines, antibody therapies, and antiviral drugs that have been approved to date were built on decades of investment in technology and basic science. We will caution that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has so far proven a straightforward test of our pandemic preparedness, and we will recommend steps we should undertake now to prepare for, to minimize the effects of, and ideally to prevent future pandemics. Other Reviews in this series describe the interactions of SARS-CoV-2 with the immune system and those therapies that target the host response to infection.

Target 2035 - update on the quest for a probe for every protein.

Twenty years after the publication of the first draft of the human genome, our knowledge of the human proteome is still fragmented. The challenge of translating the wealth of new knowledge from genomics into new medicines is that proteins, and not genes, are the primary executers of biological function. Therefore, much of how biology works in health and disease must be understood through the lens of protein function. Accordingly, a subset of human proteins has been at the heart of research interests of scientists over the centuries, and we have accumulated varying degrees of knowledge about approximately 65% of the human proteome. Nevertheless, a large proportion of proteins in the human proteome (∼35%) remains uncharacterized, and less than 5% of the human proteome has been successfully targeted for drug discovery. This highlights the profound disconnect between our abilities to obtain genetic information and subsequent development of effective medicines. Target 2035 is an international federation of biomedical scientists from the public and private sectors, which aims to address this gap by developing and applying new technologies to create by year 2035 chemogenomic libraries, chemical probes, and/or biological probes for the entire human proteome.

Opinion: Independent third-party entities as a model for validation of commercial antibodies.

A vast array of commercial antibodies covers a large percentage of human gene products, but determining which among them is most appropriate for any given application is challenging. This leads to use of non-specific antibodies that contributes to issues with reproducibility. It is our opinion that the community of scientists who use commercial antibodies in their biomedical research would benefit from third-party antibody characterization entities that use standardized operating procedures to assess and compare antibody performance. Ideally, such entities would follow the principles of open science, such that all antibodies against any given protein target would be tested in parallel, and all data generated released to the public domain without bias. Furthermore, there should be no financial incentive for the entity beyond cost-recovery. Such non-profit organizations, combined with other scientific efforts, could catalyse new discoveries by providing scientists with better validated antibody tools.

Leveraging an Open Science Drug Discovery Model to Develop CNS-Penetrant ALK2 Inhibitors for the Treatment of Diffuse Intrinsic Pontine Glioma.

There are currently no effective chemotherapeutic drugs approved for the treatment of diffuse intrinsic pontine glioma (DIPG), an aggressive pediatric cancer resident in the pons region of the brainstem. Radiation therapy is beneficial but not curative, with the condition being uniformly fatal. Analysis of the genomic landscape surrounding DIPG has revealed that activin receptor-like kinase-2 (ALK2) constitutes a potential target for therapeutic intervention given its dysregulation in the disease. We adopted an open science approach to develop a series of potent, selective, orally bioavailable, and brain-penetrant ALK2 inhibitors based on the lead compound LDN-214117. Modest structural changes to the C-3, C-4, and C-5 position substituents of the core pyridine ring afforded compounds M4K2009, M4K2117, and M4K2163, each with a superior potency, selectivity, and/or blood-brain barrier (BBB) penetration profile. Robust in vivo pharmacokinetic (PK) properties and tolerability mark these inhibitors as advanced preclinical compounds suitable for further development and evaluation in orthotopic models of DIPG.

Targeting ALK2: An Open Science Approach to Developing Therapeutics for the Treatment of Diffuse Intrinsic Pontine Glioma.

Diffuse intrinsic pontine glioma is an aggressive pediatric cancer for which no effective chemotherapeutic drugs exist. Analysis of the genomic landscape of this disease has led to the identification of the serine/threonine kinase ALK2 as a potential target for therapeutic intervention. In this work, we adopted an open science approach to develop a series of potent type I inhibitors of ALK2 which are orally bio-available and brain-penetrant. Initial efforts resulted in the discovery of M4K2009, an analogue of the previously reported ALK2 inhibitor LDN-214117. Although highly selective for ALK2 over the TGF-ßR1 receptor ALK5, M4K2009 is also moderately active against the hERG potassium channel. Varying the substituents of the trimethoxyphenyl moiety gave rise to an equipotent benzamide analogue M4K2149 with reduced off-target affinity for the ion channel. Additional modifications yielded 2-fluoro-6-methoxybenzamide derivatives (26a-c), which possess high inhibitory activity against ALK2, excellent selectivity, and superior pharmacokinetic profiles.

Implementation of an antibody characterization procedure and application to the major ALS/FTD disease gene C9ORF72.

Antibodies are a key resource in biomedical research yet there are no community-accepted standards to rigorously characterize their quality. Here we develop a procedure to validate pre-existing antibodies. Human cell lines with high expression of a target, determined through a proteomics database, are modified with CRISPR/Cas9 to knockout (KO) the corresponding gene. Commercial antibodies against the target are purchased and tested by immunoblot comparing parental and KO. Validated antibodies are used to definitively identify the most highly expressing cell lines, new KOs are generated if needed, and the lines are screened by immunoprecipitation and immunofluorescence. Selected antibodies are used for more intensive procedures such as immunohistochemistry. The pipeline is easy to implement and scalable. Application to the major ALS disease gene C9ORF72 identified high-quality antibodies revealing C9ORF72 localization to phagosomes/lysosomes. Antibodies that do not recognize C9ORF72 have been used in highly cited papers, raising concern over previously reported C9ORF72 properties.

Target 2035: probing the human proteome.

Biomedical scientists tend to focus on only a small fraction of the proteins encoded by the human genome despite overwhelming genetic evidence that many understudied proteins are important for human disease. One of the best ways to interrogate the function of a protein and to determine its relevance as a drug target is by using a pharmacological modulator, such as a chemical probe or an antibody. If these tools were available for most human proteins, it should be possible to translate the tremendous advances in genomics into a greater understanding of human health and disease, and catalyze the creation of innovative new medicines. Target 2035 is a global federation for developing and applying new technologies with the goal of creating chemogenomic libraries, chemical probes, and/or functional antibodies for the entire proteome.

Donated chemical probes for open science.

Potent, selective and broadly characterized small molecule modulators of protein function (chemical probes) are powerful research reagents. The pharmaceutical industry has generated many high-quality chemical probes and several of these have been made available to academia. However, probe-associated data and control compounds, such as inactive structurally related molecules and their associated data, are generally not accessible. The lack of data and guidance makes it difficult for researchers to decide which chemical tools to choose. Several pharmaceutical companies (AbbVie, Bayer, Boehringer Ingelheim, Janssen, MSD, Pfizer, and Takeda) have therefore entered into a pre-competitive collaboration to make available a large number of innovative high-quality probes, including all probe-associated data, control compounds and recommendations on use (https://openscienceprobes.sgc-frankfurt.de/). Here we describe the chemical tools and target-related knowledge that have been made available, and encourage others to join the project.

Discovery and structure activity relationship of the first potent cryptosporidium FIKK kinase inhibitor.

FIKKs are parasite-specific protein kinases with distinctive sequence motifs and their biological roles have not been completely elucidated. Here, we report the first potent Cryptosporidium FIKK (CpFIKK) inhibitor. We identified 4b as a potent (IC50=0.2nM) inhibitor of CpFIKK catalytic activity. In addition, we identified both CpCDPK1 selective as well as dually acting CpFIKK-CDPK1 inhibitors from the same structural class of compounds. We evaluated these CpFIKK inhibitors for inhibition of parasite growth in vitro. The observed effects on parasite growth did not correlate with CpFIKK inhibition, suggesting that CpFIKK may not be involved in parasite growth.

Optimizing Production of Antigens and Fabs in the Context of Generating Recombinant Antibodies to Human Proteins.

We developed and optimized a high-throughput project workflow to generate renewable recombinant antibodies to human proteins involved in epigenetic signalling. Three different strategies to produce phage display compatible protein antigens in bacterial systems were compared, and we found that in vivo biotinylation through the use of an Avi tag was the most productive method. Phage display selections were performed on 265 in vivo biotinylated antigen domains. High-affinity Fabs (<20nM) were obtained for 196. We constructed and optimized a new expression vector to produce in vivo biotinylated Fabs in E. coli. This increased average yields up to 10-fold, with an average yield of 4 mg/L. For 118 antigens, we identified Fabs that could immunoprecipitate their full-length endogenous targets from mammalian cell lysates. One Fab for each antigen was converted to a recombinant IgG and produced in mammalian cells, with an average yield of 15 mg/L. In summary, we have optimized each step of the pipeline to produce recombinant antibodies, significantly increasing both efficiency and yield, and also showed that these Fabs and IgGs can be generally useful for chromatin immunoprecipitation (ChIP) protocols.