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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.

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. Artificial intelligence is greatly accelerating research in drug discovery, but its development is still hindered by the lack of available data. Here the authors present data management and data science recommendations to help reach AI’s potential in the field.

ABCB10 is one of the three ATP-binding cassette (ABC) transporters found in the inner membrane of mitochondria. In mammals ABCB10 is essential for erythropoiesis, and for protection of mitochondria against oxidative stress. ABCB10 is therefore a potential therapeutic target for diseases in which increased mitochondrial reactive oxygen species production and oxidative stress play a major role. The crystal structure of apo-ABCB10 shows a classic exporter fold ABC transporter structure, in an open-inwards conformation, ready to bind the substrate or nucleotide from the inner mitochondrial matrix or membrane. Unexpectedly, however, ABCB10 adopts an open-inwards conformation when complexed with nonhydrolysable ATP analogs, in contrast to other transporter structures which adopt an open-outwards conformation in complex with ATP. The three complexes of ABCB10/ATP analogs reported here showed varying degrees of opening of the transport substrate binding site, indicating that in this conformation there is some flexibility between the two halves of the protein. These structures suggest that the observed plasticity, together with a portal between two helices in the transmembrane region of ABCB10, assist transport substrate entry into the substrate binding cavity. These structures indicate that ABC transporters may exist in an open-inwards conformation when nucleotide is bound. We discuss ways in which this observation can be aligned with the current views on mechanisms of ABC transporters.

We report an enantioselective protein affinity selection mass spectrometry screening approach (E-ASMS) that enables the detection of weak binders, informs on selectivity, and generates orthogonal confirmation of binding. After method development with control proteins, we screen 31 human proteins against a designed library of 8,217 chiral compounds. We identify 16 binders to 12 targets, including many proteins predicted to be “challenging to ligand”, and confirm their interactions through orthogonal biophysical assays. Seven binders to six targets display enantioselective binding, with K D values ranging from 3 to 20 µM. Binders for four targets (DDB1, WDR91, WDR55, and HAT1) are selected for in-depth characterization using X-ray crystallography. In all four cases, the mechanisms underlying enantioselectivity are readily explained. These results demonstrate that E-ASMS enables the identification and characterization of selective and weakly binding ligands for novel protein targets with unprecedented throughput and sensitivity. High-throughput chemical ligand discovery is challenged by false positives. Here, authors introduce a scalable enantioselective affinity-selection mass spectrometry approach for proteome-wide ligand discovery with high sensitivity and selectivity

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.

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.

The 3D structures of human therapeutic targets are enabling for drug discovery. However, their purification and crystallization remain rate determining. In individual cases, ligands have been used to increase the success rate of protein purification and crystallization, but the broad applicability of this approach is unknown. We implemented two screening platforms, based on either fluorimetry or static light scattering, to measure the increase in protein thermal stability upon binding of a ligand without the need to monitor enzyme activity. In total, 221 different proteins from humans and human parasites were screened against one or both of two sorts of small-molecule libraries. The first library comprised different salts, pH conditions, and commonly found small molecules and was applicable to all proteins. The second comprised compounds specific for protein families of particular interest (e.g., protein kinases). In 20 cases, including nine unique human protein kinases, a small molecule was identified that stabilized the proteins and promoted structure determination. The methods are cost-effective, can be implemented in any laboratory, promise to increase the success rates of purifying and crystallizing human proteins significantly, and identify new ligands for these proteins.

Mutations in the nuclear membrane zinc metalloprotease ZMPSTE24 lead to diseases of la mi ç processing (laminopathies), such as the premature aging disease progeria and metabolic disorders. ZMPSTE24 processes prelamin A, a component of the nuclear lamina intermediate filaments, by cleaving it at two sites. Failure of this processing results in accumulation of farnesylated, membrane-associated prelamin A. The 3.4 angstrom crystal structure of human ZMPSTE24 has a seven transmembrane á-helical barrel structure, surrounding a large, water-filled, intramembrane chamber, capped by a zinc metalloprotease domain with the catalytic site facing into the chamber. The 3.8 angstrom structure of a complex with a CSIM tetrapeptide showed that the mode of binding of the substrate resembles that of an insect metalloprotease inhibitor in thermolysin. Laminopathy-associated mutations predicted to reduce ZMPSTE24 activity map to the zinc metalloprotease peptide-binding site and to the bottom of the chamber.

The human cytosolic sulfotransfases (hSULTs) comprise a family of 12 phase II enzymes involved in the metabolism of drugs and hormones, the bioactivation of carcinogens, and the detoxification of xenobiotics. Knowledge of the structural and mechanistic basis of substrate specificity and activity is crucial for understanding steroid and hormone metabolism, drug sensitivity, pharmacogenomics, and response to environmental toxins. We have determined the crystal structures of five hSULTs for which structural information was lacking, and screened nine of the 12 hSULTs for binding and activity toward a panel of potential substrates and inhibitors, revealing unique \"chemical fingerprints\" for each protein. The family-wide analysis of the screening and structural data provides a comprehensive, high-level view of the determinants of substrate binding, the mechanisms of inhibition by substrates and environmental toxins, and the functions of the orphan family members SULT1C3 and SULT4A1. Evidence is provided for structural \"priming\" of the enzyme active site by cofactor binding, which influences the spectrum of small molecules that can bind to each enzyme. The data help explain substrate promiscuity in this family and, at the same time, reveal new similarities between hSULT family members that were previously unrecognized by sequence or structure comparison alone.

Inorganic polyphosphate (polyP) is a linear polymer of tens or hundreds of phosphate residues linked by high-energy bonds. It is found in all organisms and has been proposed to serve as an energy source in a pre-ATP world. This ubiquitous and abundant biopolymer plays numerous and vital roles in metabolism and regulation in prokaryotes and eukaryotes, but the underlying molecular mechanisms for most activities of polyP remain unknown. In prokaryotes, the synthesis and utilization of polyP are catalyzed by 2 families of polyP kinases, PPK1 and PPK2, and polyphosphatases. Here, we present structural and functional characterization of the PPK2 family. Proteins with a single PPK2 domain catalyze polyP-dependent phosphorylation of ADP to ATP, whereas proteins containing 2 fused PPK2 domains phosphorylate AMP to ADP. Crystal structures of 2 representative proteins, SMc02148 from Sinorhizobium meliloti and PA3455 from Pseudomonas aeruginosa, revealed a 3-layer α/β/α sandwich fold with an α-helical lid similar to the structures of microbial thymidylate kinases, suggesting that these proteins share a common evolutionary origin and catalytic mechanism. Alanine replacement mutagenesis identified 9 conserved residues, which are required for activity and include the residues from both Walker A and B motifs and the lid. Thus, the PPK2s represent a molecular mechanism, which potentially allow bacteria to use polyP as an intracellular energy reserve for the generation of ATP and survival.