High-throughput Screening Experiments Research Articles

Rational drug design targeting g-protein-coupled receptors: a structural biology perspective

G protein-coupled Receptors (G protein-coupled Receptors, GPCRs) play a key role in the transmission of extracellular signals and regulation of many biological processes, which makes these membrane proteins one of the most important classes of targets for pharmacological agents. The significant increase in the number of atomic structures of GPCRs recently has paved the way for Structure Based Drug Design (SBDD). SBDD uses information on the structure of the receptor-ligand complex to search for affinity and selective ligands without the need for high-throughput experimental ligand screening and allows a significant expansion of the chemical ligand search space. In our review we describe the process of GPCR structure obtaining by X-ray diffraction analysis and cryo-electron microscopy (cryo-EM) – an important step in rational drug design targeting GPCRs. Our main goal is to highlight to a wide range of specialists the current aspects and key features of experimental structural biology methods necessary for a detailed understanding of SBDD GPCRs.

Identification of two repurposed drugs targeting GSDMD oligomerization interface I to block pyroptosis.

As an executor of pyroptosis, gasdermin D (GSDMD) plays a critical role in inflammatory diseases and cancer. Thus, GSDMD is currently being widely explored as a drug target. Existing inhibitors targeting GSDMD, such as necrosulfonamide, disulfiram, and fumarate, primarily prevent pyroptosis by modifying human/mouse C191/C192 in the N-terminal fragment of GSDMD. However, cysteine modification can prevent the function of important proteins or enzymes, thereby leading to adverse reactions. Here, we chose an alternative key intervention site for GSDMD activation, which is located at the oligomerization interface I of its pore-forming structure. Through high-throughput virtual and experimental screening and in combination with efficacy and pharmacological validation, we have identified two safe, specific "repurposed drugs" that potently suppress GSDMD-mediated pyroptosis. Moreover, the candidates exhibited synergistic therapeutic effects of "1+ 1>2" in murine sepsis and tumorigenesis models. These recently identified GSDMD inhibitors hold great promise for clinical translation in the development of anti-inflammatory and anti-cancer immunotherapies.

Combining High-Throughput Experiments and Active Learning to Characterize Deep Eutectic Solvents.

The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring the physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious and error-prone and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs. Owing to its importance for large-scale applications, the reduction of DES viscosity through the addition of a low-molecular-weight solvent was explored as a case study. A high-throughput experimental screening was initially performed on nine different ternary DESs. Then, GPs were successfully trained to predict DES viscosity from its composition and temperature, showcasing the ability of these stochastic, nonparametric models to accurately describe the physicochemical properties of complex mixtures. Finally, the ability of GPs to provide estimates of their own uncertainty was leveraged through an AL framework to minimize the number of data points necessary to obtain accurate viscosity modes. This led to a significant reduction in data requirements, with many systems requiring only five independent viscosity data points to be properly described.

Harnessing the Biomimetic Effect of Macromolecular Crowding in the Cell-Derived Model of Clubfoot Fibrosis.

Fibrotic changes in pediatric clubfoot provide an opportunity to improve corrective therapy and prevent relapses with targeted drugs. This study defines the parameters of clubfoot fibrosis and presents a unique analysis of a simple pseudo-3D in vitro model for disease-specific high-throughput drug screening experiments. The model combines clubfoot-derived fibroblasts with a biomimetic cultivation environment induced by the water-soluble polymers Ficoll and Polyvinylpyrrolidone, utilizing the principle of macromolecular crowding. We achieved higher conversion of soluble collagen into insoluble collagen, accelerated formation of the extracellular matrix layer and upregulated fibrosis-related genes in the mixed Ficoll environment. To test the model, we evaluated the effect of a potential antifibrotic drug, minoxidil, emphasizing collagen content and cross-linking. While the model amplified overall collagen deposition, minoxidil effectively blocked the expression of lysyl hydroxylases, which are responsible for the increased occurrence of specific collagen cross-linking in various fibrotic tissues. This limited the formation of collagen cross-link in both the model and control environments. Our findings provide a tool for expanding preclinical research for clubfoot and similar fibroproliferative conditions.

PLM_Sol: predicting protein solubility by benchmarking multiple protein language models with the updated Escherichia coli protein solubility dataset.

Protein solubility plays a crucial role in various biotechnological, industrial, and biomedical applications. With the reduction in sequencing and gene synthesis costs, the adoption of high-throughput experimental screening coupled with tailored bioinformatic prediction has witnessed a rapidly growing trend for the development of novel functional enzymes of interest (EOI). High protein solubility rates are essential in this process and accurate prediction of solubility is a challenging task. As deep learning technology continues to evolve, attention-based protein language models (PLMs) can extract intrinsic information from protein sequences to a greater extent. Leveraging these models along with the increasing availability of protein solubility data inferred from structural database like the Protein Data Bank holds great potential to enhance the prediction of protein solubility. In this study, we curated an Updated Escherichia coli protein Solubility DataSet (UESolDS) and employed a combination of multiple PLMs and classification layers to predict protein solubility. The resulting best-performing model, named Protein Language Model-based protein Solubility prediction model (PLM_Sol), demonstrated significant improvements over previous reported models, achieving a notable 6.4% increase in accuracy, 9.0% increase in F1_score, and 11.1% increase in Matthews correlation coefficient score on the independent test set. Moreover, additional evaluation utilizing our in-house synthesized protein resource as test data, encompassing diverse types of enzymes, also showcased the good performance of PLM_Sol. Overall, PLM_Sol exhibited consistent and promising performance across both independent test set and experimental set, thereby making it well suited for facilitating large-scale EOI studies. PLM_Sol is available as a standalone program and as an easy-to-use model at https://zenodo.org/doi/10.5281/zenodo.10675340.

Rational Design of Drugs Targeting G-Protein-Coupled Receptors: A Structural Biology Perspective.

G protein-coupled receptors (GPCRs) play a key role in the transduction of extracellular signals to cells and regulation of many biological processes, which makes these membrane proteins one of the most important targets for pharmacological agents. A significant increase in the number of resolved atomic structures of GPCRs has opened the possibility of developing pharmaceuticals targeting these receptors via structure-based drug design (SBDD). SBDD employs information on the structure of receptor-ligand complexes to search for selective ligands without the need for an extensive high-throughput experimental ligand screening and can significantly expand the chemical space for ligand search. In this review, we describe the process of deciphering GPCR structures using X-ray diffraction analysis and cryoelectron microscopy as an important stage in the rational design of drugs targeting this receptor class. Our main goal was to present modern developments and key features of experimental methods used in SBDD of GPCR-targeting agents to a wide range of specialists.

HTSplotter: An end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening.

In biomedical research, high-throughput screening is often applied as it comes with automatization, higher-efficiency, and more and faster results. High-throughput screening experiments encompass drug, drug combination, genetic perturbagen or a combination of genetic and chemical perturbagen screens. These experiments are conducted in real-time assays over time or in an endpoint assay. The data analysis consists of data cleaning and structuring, as well as further data processing and visualisation, which, due to the amount of data, can easily become laborious, time-consuming and error-prone. Therefore, several tools have been developed to aid researchers in this process, but these typically focus on specific experimental set-ups and are unable to process data of several time points and genetic-chemical perturbagen screens. To meet these needs, we developed HTSplotter, a web tool and Python module that performs automatic data analysis and visualization of visualization of eitherendpoint or real-time assays from different high-throughput screening experiments: drug, drug combination, genetic perturbagen and genetic-chemical perturbagen screens. HTSplotter implements an algorithm based on conditional statements to identify experiment types and controls. After appropriate data normalization, including growth rate normalization, HTSplotter executes downstream analyses such as dose-response relationship and drug synergism assessment by the Bliss independence (BI), Zero Interaction Potency (ZIP) and Highest Single Agent (HSA) methods. All results are exported as a text file and plots are saved in a PDF file. The main advantage of HTSplotter over other available tools is the automatic analysis of genetic-chemical perturbagen screens and real-time assays where growth rate and perturbagen effect results are plotted over time. In conclusion, HTSplotter allows for the automatic end-to-end data processing, analysis and visualisation of various high-throughput in vitro cell culture screens, offering major improvements in terms of versatility, efficiency and time over existing tools.

Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.

Suzuki cross-coupling reactions are considered a valuable tool for constructing carbon-carbon bonds in small molecule drug discovery. However, the synthesis of chemical matter often represents a time-consuming and labour-intensive bottleneck. We demonstrate how machine learning methods trained on high-throughput experimentation (HTE) data can be leveraged to enable fast reaction condition selection for novel coupling partners. We show that the trained models support chemists in determining suitable catalyst-solvent-base combinations for individual transformations including an evaluation of the need for HTE screening. We introduce an algorithm for designing 96-well plates optimized towards reaction yields and discuss the model performance of zero- and few-shot machine learning. The best-performing machine learning model achieved a three-category classification accuracy of 76.3% (±0.2%) and an F 1-score for a binary classification of 79.1% (±0.9%). Validation on eight reactions revealed a receiver operating characteristic (ROC) curve (AUC) value of 0.82 (±0.07) for few-shot machine learning. On the other hand, zero-shot machine learning models achieved a mean ROC-AUC value of 0.63 (±0.16). This study positively advocates the application of few-shot machine learning-guided reaction condition selection for HTE campaigns in medicinal chemistry and highlights practical applications as well as challenges associated with zero-shot machine learning.

C. elegans: A potent model for high-throughput screening experiments investigating the FLASH effect

This study explores the effects of UHDR irradiation on Caenorhabditis elegans embryos. UHDR proton and electron beams demonstrate a sparing effect, aligning with literature findings. This highlights C. eleganssuitability as a screening model for studying the LET impact on the FLASH effect, reinforcing its potential in radiation research.

Machine Learning Accelerated Exploration of Ternary Organic Heterojunction Photocatalysts for Sacrificial Hydrogen Evolution.

Donor-acceptor heterojunctions in organic photocatalysts can provide enhanced exciton dissociation and charge separation, thereby improving the photocatalytic activity. However, the wide choice of possible donors and acceptors poses a challenge for the rational design of organic heterojunction photocatalysts, particularly for large ternary phase spaces. We accelerated the exploration of ternary organic heterojunction photocatalysts (TOHP) by using a combination of machine learning and high-throughput experimental screening. This involved 736 experiments in all, out of possible 4320 ternary combinations. The top ten most active TOHPs discovered using this strategy showed outstanding sacrificial hydrogen production rates of more than 500 mmol g-1 h-1, with the most active ternary material reaching a rate of 749.8 mmol g-1 h-1 under 1 sun illumination. These rates of photocatalytic hydrogen generation are among the highest reported for organic photocatalysts in the literature.

Tuning and high throughput experimental screening of eutectic electrolytes with co-solvents for redox flow batteries

Eutectic solvents, with high salt concentrations and suppressed volatility, are promising alternatives to aqueous and volatile organic electrolytes for grid-scale energy storage devices such as redox flow batteries (RFBs). However, it is not known a priori which hydrogen bond acceptors and donors would form a eutectic solvent and at which compositions of these mixtures would yield desirable properties for RFBs. Specifically, the known deep eutectic solvents generally have high viscosities and low ionic conductivities, especially in the landscape of RFB electrolytes. Here, a high throughput experimental (HTE) study was carried out to assess the properties of eutectic solvents based on choline chloride as the hydrogen bond acceptor and ethylene glycol and aniline as the hydrogen bond donors. In addition, water, acetonitrile, and dimethyl sulfoxide as co-solvents were examined in terms of tuning the viscosity, conductivity, and solubility of two redox-active species: methyl viologen dichloride hydrate (MVCl2·H2O, or MV for simplicity) and 2,1,3-benzothiadiazole (BTZ) that are relevant to RFBs. The HTE framework enabled the development of a rapid and comprehensive understanding of the design of new eutectic electrolytes incorporating co-solvents to improve the physicochemical and electrochemical properties for RFBs.

A Normalization Protocol Reduces Edge Effect in High-Throughput Analyses of Hydroxyurea Hypersensitivity in Fission Yeast.

Edge effect denotes better growth of microbial organisms situated at the edge of the solid agar media. Although the precise reason underlying edge effect is unresolved, it is generally attributed to greater nutrient availability with less competing neighbors at the edge. Nonetheless, edge effect constitutes an unavoidable confounding factor that results in misinterpretation of cell fitness, especially in high-throughput screening experiments widely employed for genome-wide investigation using microbial gene knockout or mutant libraries. Here, we visualize edge effect in high-throughput high-density pinning arrays and report a normalization approach based on colony growth rate to quantify drug (hydroxyurea)-hypersensitivity in fission yeast strains. This normalization procedure improved the accuracy of fitness measurement by compensating cell growth rate discrepancy at different locations on the plate and reducing false-positive and -negative frequencies. Our work thus provides a simple and coding-free solution for a struggling problem in robotics-based high-throughput screening experiments.

Charge–discharge properties of LiMn 2 O 4 -group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models

Charge–discharge properties of LiMn ₂ O ₄ -group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models

An Experimental High-Throughput to High-Fidelity Study Towards Discovering Al–Cr Containing Corrosion-Resistant Compositionally Complex Alloys

Compositionally complex alloys hold the promise of simultaneously attaining superior combinations of properties, such as corrosion resistance, light-weighting, and strength. Achieving this goal is a challenge due in part to a large number of possible compositions and structures in the vast alloy design space. High-throughput methods offer a path forward, but a strong connection between the synthesis of an alloy of a given composition and structure with its properties has not been fully realized to date. Here, we present the rapid identification of corrosion-resistant alloys based on combinations of Al and Cr in a base Al–Co–Cr–Fe–Ni alloy. Previously unstudied alloy stoichiometries were identified using a combination of high-throughput experimental screening coupled with key metallurgical and electrochemical corrosion tests, identifying alloys with excellent passivation behavior. The alloy native oxide performance and its self-healing attributes were probed using rapid tests in deaerated 0.1-mol/L H2SO4. Importantly, a correlation was found between the electrochemical impedance modulus of the exposure-modified air-formed film and self-healing rate of the CCAs. Multi-element extended x-ray absorption fine structure analyses connected more ordered type chemical short-range order in the Ni–Al 1st nearest-neighbor shell to poorer corrosion resistance. This report underscores the utility of high-throughput exploration of compositionally complex alloys for the identification and rapid screening of a vast stoichiometric space.Graphical

Photo-Electrochemical Oxygen Evolution Activity in Nickel and Cobalt Antimonates

Renewable generation of fuels via solar energy offers promising pathways towards sustainable energy future. Its deployment hinges on the discovery of electrochemically durable materials with good solar-to-chemical conversion efficiency. Necessary visible spectrum photoresponse from electrochemically stable materials is however quite rare. On the other hand, the oxygen evolution reaction (OER) requires electrodes that are not only catalytically active, but also stable under harsh electrochemical environments. In this talk, we report on and theoretical understanding of photo-electrochemical behavior an amorphous and crystaline versions of Ni-Sb-Ox and Co-Sb-Ox oxide photoanodes discovered via high-throughput experimental screening [1,2]. The newly discovered amorphous phases meet the requirements of operational stability, visible photoresponse, and appreciable photovoltage. Guided by experimental X-ray absorption characterization of these systems we use density functional theory calculations to perform a prototype phase search to identify a broad family of stable and metastable mixed rutile and hexagonal-like phases for other compositions [3]. Detailed Pourbaix analysis of the identified phases show excellent electrochemical stability, consistent with experimentally measured electrolyte concentrations of photoelectrochemical cells. We analyze when he identified phases form favorable oxygen vacancies formation energies under the reducing synthesis conditions which match measured Ni K-edge x-ray absorption spectra. The calculated OER overpotentials for the most active sites decreases with increasing Ni/Co content, which captures the experimentally observed trend. Stable amorphous metal-antimonates photoanodes are important systems for computational understanding of both the structural and catalytic behavior of these complex oxides and elucidating the co-optimization of photo-electrochemical activity and durability. This material is based on work performed by the Liquid Sunlight Alliance, which is supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Fuels from Sunlight Hub under award DE-SC0021266.[1] Zhou, L., Peterson, E. A., Rao, K. K., Lu, Y., Li, X., Lai, Y., Bauers, S. R., Richter, M. H., Kan, K., Wang, Y., Newhouse, P. F., Yano, J., Neaton, J. B., Bajdich, M., & Gregoire, J. M. Addressing solar photochemistry durability with an amorphous nickel antimonate photoanode. Cell Reports Physical Science, 3(7), 100959. (2022).[2] Zhou, L., Wang, Y., Kan, K., Lucana, D. M., Guevarra, D., Lai, Y., & Gregoire, J. M. Surveying Metal Antimonate Photoanodes for Solar Fuel Generation. ACS Sustainable Chemistry and Engineering, 21, 33. (2022).[3] Rao, K. K., Zhou, L., Lai, Y., Richter, M. H., Li, X., Lu, Y., Yano, J., Gregoire, J. M. Bajdich, M., Resolving Atomistic Structure and Oxygen Evolution Activity in Nickel Antimonates, (under review).

Studying Intermolecular Interactions in an Antibody-Drug Conjugate Through Chemical Screening and Computational Modeling

Antibody-drug conjugates (ADCs) combine the selectivity of antibodies with the cytotoxicity of drug payloads to yield highly targeted and potent therapeutics. Owing to the need to chemically modify residues for attachment of the payload and their more complex structure compared to either component alone, ADCs can present additional challenges related to stability of the final drug product. Here, we report for the first time the use of high-throughput experimental screens and computational techniques to tune the conformational and colloidal behavior of a monomethyl auristatin F-based ADC. The ADC, which exhibits high opalescence with strongly attractive protein-protein interactions, is transformed into a more stable structure by experimentally traversing a library of more than ∼100 formulations. A significant reduction in turbidity and increase in diffusion interaction parameter is observed by varying properties such as pH and ionic strength. Computational modeling rationalized these changes and pointed to the presence of attractive electrostatic interactions between ADC molecules facilitated by the drug payload and histidine residues. Taken together, the experimental and computational work presented provides a general roadmap of studies to perform during ADC development to find stable formulations, while the mechanistic learnings can be applied towards the design and stabilization of other IgG1-based ADCs.

Importance of Process Variables and Their Optimization for Oxidative Coupling of Methane (OCM).

Oxidative coupling of methane (OCM) is a promising process for converting natural gas into high-value chemicals such as ethane and ethylene. The process, however, requires important improvements for commercialization. The foremost is increasing the process selectivity to C2 (C2H4 + C2H6) at moderate to high levels of methane conversion. These developments are often addressed at the catalyst level. However, optimization of process conditions can lead to very important improvements. In this study, a high-throughput screening (HTS) instrument was utilized for La2O3/CeO2 (3.3 mol % Ce) to generate a parametric data set within the temperature range of 600-800 °C, CH4/O2 ratio between 3 and 13, pressure between 1 and 10 bar, and catalyst loading between 5 and 20 mg leading to space-time between 40 and 172 s. Statistical design of experiments (DoE) was applied to gain insights into the effect of operating parameters and to determine the optimal operating conditions for maximum production of ethane and ethylene. Rate-of-production analysis was used to shed light on the elementary reactions involved in different operating conditions. The data obtained from HTS experiments established quadratic equations relating the studied process variables and output responses. The quadratic equations can be used to predict and optimize the OCM process. The results demonstrated that the CH4/O2 ratio and operating temperatures are key for controlling the process performance. Operating at higher temperatures with high CH4/O2 ratios increased the selectivity to C2 and minimized COx (CO + CO2) at moderate conversion levels. In addition to process optimization, DoE results also allowed the flexibility of manipulating the performance of OCM reaction products. A C2 selectivity of 61% and a methane conversion of 18% were found to be optimum at 800 °C, a CH4/O2 ratio of 7, and a pressure of 1 bar.

BinderSpace: A package for sequence space analyses for datasets of affinity-selected oligonucleotides and peptide-based molecules.

Discovery of target-binding molecules, such as aptamers and peptides, is usually performed with the use of high-throughput experimental screening methods. These methods typically generate large datasets of sequences of target-binding molecules, which can be enriched with high affinity binders. However, the identification of the highest affinity binders from these large datasets often requires additional low-throughput experiments or other approaches. Bioinformatics-based analyses could be helpful to better understand these large datasets and identify the parts of the sequence space enriched with high affinity binders. BinderSpace is an open-source Python package that performs motif analysis, sequence space visualization, clustering analyses, and sequence extraction from clusters of interest. The motif analysis, resulting in text-based and visual output of motifs, can also provide heat maps of previously measured user-defined functional properties for all the motif-containing molecules. Users can also run principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) analyses on whole datasets and on motif-related subsets of the data. Functionally important sequences can also be highlighted in the resulting PCA and t-SNE maps. If points (sequences) in two-dimensional maps in PCA or t-SNE space form clusters, users can perform clustering analyses on their data, and extract sequences from clusters of interest. We demonstrate the use of BinderSpace on a dataset of oligonucleotides binding to single-wall carbon nanotubes in the presence and absence of a bioanalyte, and on a dataset of cyclic peptidomimetics binding to bovine carbonic anhydrase protein. BinderSpace is openly accessible to the public via the GitHub website: https://github.com/vukoviclab/BinderSpace.

Abstract 441: Targeting MKK3-MYC complex to reduce racial survival disparity in breast cancer

Abstract Breast cancer is the most common cancer and the leading cause of cancer-related death in women. The triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer. African American women experience up to 2-fold higher incidence of TNBC as compared to White women. Moreover, African Americans die from breast cancer at up to 40% higher rate than White and Hispanic women. However, there is no therapeutic options are currently available to reduce survival disparity in breast cancer. To address this unmet medical need, we develop novel computational methods combined with experimental chemical biology and high-throughput screening (HTS) technologies. Using our specially designed bioinformatics workflow, we discovered that high expression of mitogen-activated protein kinase kinase 3 (MKK3) strongly correlates with worsened clinical outcomes in African American TNBC patients. The analysis of TNBC patient genomic profiles revealed that MKK3 promotes TNBC in part by activating the major tumor driver transcription factor MYC. MYC is a highly appealing therapeutic target in TNBC, however, no approved MYC inhibitors are available. We found that MKK3 binds and activates MYC through protein-protein interaction (PPI) in TNBC cells. Both genetic and pharmacological inhibition of the MKK3/MYC complex suppresses MYC activity and the viability of TNBC cells. Thus, MKK3/MYC interaction represents a novel vulnerability for therapeutic intervention in TNBC. By combining computational modeling with experimental HTS, we have discovered the first small molecule MKK3/MYC PPI inhibitors. For example, a quinoline derivative SGI-1027 disrupts the MKK3/MYC complex in cells and in vitro, inhibits MYC transcriptional activity, and suppresses the viability of TNBC cells. Together, our studies revealed a novel mechanism of TNBC tumorigenesis through MKK3-induced MYC activation, establish a new HTS to enable the discovery of potent and selective inhibitors to regulate MKK3/MYC complex, and may open new clinical strategies to reduce racial disparity in TNBC mortality. Acknowledgments: This work was supported in part by Mary Kay Ash Foundation Grant for Cancer Research (A.A.I.), Emory initiative on Biological Discovery through Chemical Innovation (A.A.I.), NCI’s Informatics Technology for Cancer Research (ITCR) Program (R21CA274620, A.A.I.), NCI Cancer Target Discovery and Development (CTD^2) Network (U01CA217875, H.F.), NCI Emory Lung Cancer SPORE (P50CA217691, H.F.), Career Enhancement Program (A.A.I., P50CA217691), Winship Cancer Institute (NIH 5P30CA138292).

Intra-Domain Residue Coevolution in Transcription Factors Contributes to DNA Binding Specificity.

Understanding the basis of the DNA-binding specificity of transcription factors (TFs) has been of long-standing interest. Despite extensive efforts to map millions of putative TF binding sequences, identifying the critical determinants for DNA binding specificity remains a major challenge. The coevolution of residues in proteins occurs due to a shared evolutionary history. However, it is unclear how coevolving residues in TFs contribute to DNA binding specificity. Here, we systematically collected publicly available data sets from multiple large-scale high-throughput TF-DNA interaction screening experiments for the major TF families with large numbers of TF members. These families included the Homeobox, HLH, bZIP_1, Ets, HMG_box, ZF-C4, and Zn_clus TFs. We detected TF subclass-determining sites (TSDSs) and showed that the TSDSs were more likely to coevolve with other TSDSs than with non-TSDSs, particularly for the Homeobox, HLH, Ets, bZIP_1, and HMG_box TF families. By in silico modeling, we showed that mutation of the highly coevolving residues could significantly reduce the stability of the TF-DNA complex. The distant residues from the DNA interface also contributed to TF-DNA binding activity. Overall, our study gave evidence that coevolved residues relate to transcriptional regulation and provided insights into the potential application of engineered DNA-binding domains and proteins. IMPORTANCE While unraveling DNA-binding specificity of TFs is the key to understanding the basis and molecular mechanism of gene expression regulation, identifying the critical determinants that contribute to DNA binding specificity remains a major challenge. In this study, we provided evidence showing that coevolving residues in TF domains contributed to DNA binding specificity. We demonstrated that the TSDSs were more likely to coevolve with other TSDSs than with non-TSDSs. Mutation of the coevolving residue pairs (CRPs) could significantly reduce the stability of THE TF-DNA complex, and even the distant residues from the DNA interface contribute to TF-DNA binding activity. Collectively, our study expands our knowledge of the interactions among coevolved residues in TFs, tertiary contacting, and functional importance in refined transcriptional regulation. Understanding the impact of coevolving residues in TFs will help understand the details of transcription of gene regulation and advance the application of engineered DNA-binding domains and protein.