T. Andrew Binkowski’s research while affiliated with University of Chicago and other places

Simple Summary Drugs work by binding to a specific 3D structure on a protein. Drug discovery has historically been driven by prior knowledge of function, either of a protein or chemical. This knowledge of function then drives investigations to probe chemical/protein interactions. We undertook a different approach. We first identified unique 3D structures, agnostic of function, and investigated whether they could lead us to innovative therapeutics. Using a synchrotron-based X-ray source, we first determined high-resolution structures of hundreds of proteins. With a supercomputer running analytical programs created by us, we identified novel 3D structures and screened for chemicals binding them. We then tested their ability to inhibit cancer growth without damaging normal cells. We identified a potent inhibitor of a deadly cancer, melanoma. It was not toxic to normal cells even at 2100-fold higher doses. It worked by inducing anoikis, a fundamental process of known importance for cancer. Therapeutics that selectively induce anoikis are needed. In summary, we demonstrate the power of using a 3D protein structure as the starting point to discover new biology and drugs. Abstract Drug discovery historically starts with an established function, either that of compounds or proteins. This can hamper discovery of novel therapeutics. As structure determines function, we hypothesized that unique 3D protein structures constitute primary data that can inform novel discovery. Using a computationally intensive physics-based analytical platform operating at supercomputing speeds, we probed a high-resolution protein X-ray crystallographic library developed by us. For each of the eight identified novel 3D structures, we analyzed binding of sixty million compounds. Top-ranking compounds were acquired and screened for efficacy against breast, prostate, colon, or lung cancer, and for toxicity on normal human bone marrow stem cells, both using eight-day colony formation assays. Effective and non-toxic compounds segregated to two pockets. One compound, Dxr2-017, exhibited selective anti-melanoma activity in the NCI-60 cell line screen. In eight-day assays, Dxr2-017 had an IC50 of 12 nM against melanoma cells, while concentrations over 2100-fold higher had minimal stem cell toxicity. Dxr2-017 induced anoikis, a unique form of programmed cell death in need of targeted therapeutics. Our findings demonstrate proof-of-concept that protein structures represent high-value primary data to support the discovery of novel acting therapeutics. This approach is widely applicable.

Introduction: Drug action is mediated by a small molecule therapeutic binding to a specific structural motif on a protein. As structure determines function, it follows that unique structural motifs are enriched for sites of unique function and high therapeutic target value. We hypothesized that protein structure constitutes high value primary information that can drive the discovery of novel therapeutic agents. Method: We developed a unique physics-based protein structure analysis platform scaled to run on a supercomputer, allowing us to consider dynamic protein flexibility and the influence of solvent upon protein structure, and to probe large protein libraries as a primary source of structure information that can support up-front structure-based screens. We developed and then probed a high resolution protein x-ray crystallographic library, created and curated by us. From a 60 million compound library, we conducted virtual screens for docking to identified sites. 8-day colony formation assays were conducted on a panel of 8 human breast, prostate, lung and colon cancer cell lines, as well as 8- and 14-day trilineage hematopoietic colony formation assays on human cord blood stem cells. Findings: From the first 180 deposited protein structures, we screened for unique protein surface structure. Structures were deemed unique if they were not otherwise known to be associated with any particular functional roles and were not the binding site of known therapeutics. Further selection included those that were clefts and possessed physical characteristics compatible with binding small chemicals whose properties have been linked to effective therapeutics. For each of 8 sites on 6 different proteins, a suite of analytics probed docking solutions from the compound library, generating a ranked list of 100 compounds for each site. Based on factors inclusive of availability and low potential for toxicity, compounds were acquired and used in in-parallel selection and de-selection functional assays. Cancer cell growth inhibition was a positive selection criterion, with additional ranking based on greater potency and broader efficacy. Bone marrow toxicity is limiting for most drugs, and inhibition of bone marrow stem cell growth was as a deselection criterion. Multiple compounds clustered around 2 sites on 2 different proteins. One compound, Dxr2-017, exhibited very high relative efficacy against colon and breast cancer cells. Evaluation of Dxr2-017 in the NCI-60 panel corroborated these findings, and showed even higher efficacy against melanoma. We demonstrated that Dxr2-017 inhibited melanoma growth with low nanomolar efficacy, while concentrations of over 2300-fold higher had only limited inhibitory effects on bone marrow stem cells. Conclusions: This study provides proof-of-principle that protein structure constitutes high value primary information that can support identification of novel therapeutics. This approach is widely applicable and expandable. Citation Format: Fangfang Qiao, T. Andrew Binkowski, Wayne Anderson, Weining Chen, Gray Schiltz, Karl Scheidt, Amarnath Natarajan, Raymond Bergan. Protein structure inspired drug discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4493.

Curriculum development is dependent on the following question: What are the learning goals for a specific topic, and what are reasonable ways to organize and order those goals? Learning trajectories (LTs) for computational thinking (CT) topics will help to guide emerging curriculum development efforts for computer science in elementary school. This study describes the development of an LT for Debugging. We conducted a rigorous analysis of scholarly research on K-8 computer science education to extract what concepts in debugging students should and are capable of learning. The concepts were organized into the LT presented within. In this paper, we describe the three dimensions of debugging that emerged during the creation of the trajectory: (1) strategies for finding and fixing errors, (2) types of errors, and (3) the role of errors in problem solving. In doing so, we go beyond identification of specific debugging strategies to further articulate knowledge that would help students understand when to use those techniques and why they are successful. Finally, we illustrate how the Debugging LT has guided our efforts to develop an integrated mathematics and CT curriculum for grades 3-5.

As new initiatives in computational thinking and computer science (CS/CT) are being developed and deployed, it is important to identify and understand the key concepts that are essential for student learning. In this study, we present the phases of construction of a learning trajectory (LT) for Decomposition in the context of CS/CT in K-8 education. From an extensive literature review, 63 learning goals representative of decomposition understanding and practices were identified and synthesized into 13 consensus goals. The focus of this paper is how relationships between these consensus goals were identified and used to place the goals into a learning trajectory. We discuss the theories and frameworks that guided the trajectory's construction as well as the methodology and justifications used to draw pathways through the trajectory in each phase. Finally, we discuss potential uses for the trajectory and suggest further explorations for decomposition in CS/CT.

Computing curricula are being developed for elementary school classrooms, yet research evidence is scant for learning trajectories that drive curricular decisions about what topics should be addressed at each grade level, at what depth, and in what order. This study presents learning trajectories based on an in-depth review of over 100 scholarly articles in computer science education research. We present three levels of results. First, we present the characteristics of the 600+ learning goals and their research context that affected the learning trajectory creation process. Second, we describe our first three learning trajectories (Sequence, Repetition, and Conditionals), and the relationship between the learning goals and the resulting trajectories. Finally, we discuss the ways in which assumptions about the context (mathematics) and language (e.g., Scratch) directly influenced the trajectories.

Allogeneic hematopoietic cell transplantation (HCT) offers the potential to cure hematologic malignancies. In the absence of an HLA-matched donor, HLA mismatched unrelated donors may be used, although risks of GvHD and treatment-related mortality (TRM) are higher. Identification and avoidance of amino-acid substitution and position types (AASPT) conferring higher risks of TRM and GvHD would potentially improve the success of transplantation from single HLA mismatched unrelated donors. Using random forest and logistic regression analyses, we identified 19 AASPT associated with greater risks for at least one adverse transplant outcome: grade III-IV acute GvHD, TRM, lower disease-free survival or worse overall survival relative to HLA-matched unrelated donors and to other AASPT. When tested in an independent validation cohort of 3530 patients, none of the AASPT from the training set were validated as high risk, however. Review of the literature shows that failure to validate original observations is the rule and not the exception in immunobiology and emphasizes the importance of independent validation before clinical application. Our current data do not support avoiding any specific class I AASPT for unrelated donors. Additional studies should be performed to fully understand the role of AASPT in HCT outcomes.Bone Marrow Transplantation advance online publication, 23 May 2016; doi:10.1038/bmt.2016.142.

Efficiently porting ordinary applications to Blue Gene/Q supercomputers is a significant challenge. Codes are often originally developed without considering advanced architectures and related tool chains. Science needs frequently lead users to want to run large numbers of relatively small jobs (of- ten called many-task computing, an ensemble, or a workflow), which can conflict with supercomputer configurations. In this paper, we discuss techniques developed to execute ordinary applications over leadership class supercomputers. We use the high-performance Swift parallel scripting framework and build two workflow execution techniques–sub-jobs and main-wrap. The sub-jobs technique, built on top of the IBM Blue Gene/Q resource manager Cobalt’s sub-block jobs, lets users submit multiple, independent, repeated smaller jobs within a single larger resource block. The main-wrap technique is a scheme that enables C/C++ programs to be defined as functions that are wrapped by a high-performance Swift wrapper and that are invoked as a Swift script. We discuss the needs, benefits, technicalities, and current limitations of these techniques. We further discuss the real-world science enabled by these techniques and the results obtained.

Unlabelled: The Human Proteome Project (HPP) is designed to generate a comprehensive map of the protein-based molecular architecture of the human body, to provide a resource to help elucidate biological and molecular function, and to advance diagnosis and treatment of diseases. Within this framework, the chromosome-based HPP (C-HPP) has allocated responsibility for mapping individual chromosomes by country or region, while the biology/disease HPP (B/D-HPP) coordinates these teams in cross-functional disease-based groups. Chromosome 6 (Ch6) provides an excellent model for integration of these two tasks. This metacentric chromosome has a complement of 1002-1034 genes that code for known, novel or putative proteins. Ch6 is functionally associated with more than 120 major human diseases, many with high population prevalence, devastating clinical impact and profound societal consequences. The unique combination of genomic, proteomic, metabolomic, phenomic and health services data being drawn together within the Ch6 program has enormous potential to advance personalized medicine by promoting robust biomarkers, subunit vaccines and new drug targets. The strong liaison between the clinical and laboratory teams, and the structured framework for technology transfer and health policy decisions within Canada will increase the speed and efficacy of this transition, and the value of this translational research. Biological significance: Canada has been selected to play a leading role in the international Human Proteome Project, the global counterpart of the Human Genome Project designed to understand the structure and function of the human proteome in health and disease. Canada will lead an international team focusing on chromosome 6, which is functionally associated with more than 120 major human diseases, including immune and inflammatory disorders affecting the brain, skeletal system, heart and blood vessels, lungs, kidney, liver, gastrointestinal tract and endocrine system. Many of these chronic and persistent diseases have a high population prevalence, devastating clinical impact and profound societal consequences. As a result, they impose a multi-billion dollar economic burden on Canada and on all advanced societies through direct costs of patient care, the loss of health and productivity, and extensive caregiver burden. There is no definitive treatment at the present time for any of these disorders. The manuscript outlines the research which will involve a systematic assessment of all chromosome 6 genes, development of a knowledge base, and development of assays and reagents for all chromosome 6 proteins. We feel that the informatic infrastructure and MRM assays developed will place the chromosome 6 consortium in an excellent position to be a leading player in this major international research initiative. This article is part of a Special Issue: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

In Structural Genomics projects, virtual high-throughput ligand screening can be utilized to provide important functional details for newly determined protein structures. Using a variety of publicly available software tools, it is possible to computationally model, predict, and evaluate how different ligands interact with a given protein. At the Center for Structural Genomics of Infectious Diseases (CSGID) a series of protein analysis, docking and molecular dynamics software is scripted into a single hierarchical pipeline allowing for an exhaustive investigation of protein-ligand interactions. The ability to conduct accurate computational predictions of protein-ligand binding is a vital component in improving both the efficiency and economics of drug discovery. Computational simulations can minimize experimental efforts, the slowest and most cost prohibitive aspect of identifying new therapeutics.