Approaching Pharmacological Space: Events and Components.

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

A natural adhesive-based nanomedicine initiates photothermal-directed in situ immunotherapy with durability and maintenance.

Tumor immunotherapies have emerged as a promising frontier in the realm of cancer treatment. However, challenges persist in achieving localized, durable immunostimulation while counteracting the tumor's immunosuppressive environment. Here, we develop a natural mussel foot protein-based nanomedicine with spatiotemporal control for tumor immunotherapy. In this nanomedicine, an immunoadjuvant prodrug and a photosensitizer are integrated, which is driven by their dynamic bonding and non-covalent assembling with the protein carrier. Harnessing the protein carrier's bioadhesion, this nanomedicine achieves a drug co-delivery with spatiotemporal precision, by which it not only promotes tumor photothermal ablation but also broadens tumor antigen repertoire, facilitating in situ immunotherapy with durability and maintenance. This nanomedicine also modulates the tumor microenvironment to overcome immunosuppression, thereby amplifying antitumor responses against tumor progression. Our strategy underscores a mussel foot protein-derived design philosophy of drug delivery aimed at refining combinatorial immunotherapy, offering insights into leveraging natural proteins for cancer treatment.

[Mechanism of liver injury induced by alcohol extract of salt-processed Psoraleae Fructus based on chemical modification of protein].

Salt-processed Psoraleae Fructus is a commonly used tonic in clinical practice. However, its usage is restricted due to the inherent toxicity. The covalent modification of proteins by reactive metabolites(RMs) plays a role in the hepatotoxicity of salt-processed Psoraleae Fructus. This study delves into the protein covalent modification by RMs generated from psoralen/isopsoralen, the primary toxic components of salt-processed Psoraleae Fructus, by liquid chromatography-mass spectrometry(LC-MS), aiming to elucidate the mechanism underlying the hepatic injury induced by salt-processed Psoraleae Fructus. Biochemical methods were utilized to measure the levels of alanine aminotransferase(ALT), aspartate aminotransferase(AST), catalase(CAT), malondialdehyde(MDA), superoxide dismutase(SOD), reduced glutathione(GSH), and glutathione S-transferase(GST) in mice. The pathological changes in the liver were observed by hematoxylin-eosin(HE) staining. Subsequently, ultra performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry(UPLC-Q-TOF-MS) was employed to identify the primary toxic components of psoralen/isopsoralen and the RMs in salt-processed Psoraleae Fructus. Covalent bonding adducts of the toxic components/RMs with GSH and free amino acids were identified to investigate the effects of the toxic components on modification sites and patterns of amino acids. The modifications of RMs were incorporated into the variable modifications of Proteome Discoverer, and the target proteins of psoralen/isopsoralen were detected by liquid chromatography-quadrupole exactive-mass spectrometry. Lastly, Label-free quantitative proteomics was adopted to screen differential proteins, which were further subjected to KEGG and GO enrichment analyses and confirmed by qPCR. The results indicated that compared with the control group, salt-processed Psoraleae Fructus significantly elevated the ALT, AST, and MDA levels and lowered the SOD, CAT, GSH, and GST levels in a dose-dependent manner, while causing obvious vacuolization and inflammatory cell infiltration in mouse hepatocytes. Furthermore, the livers of mice in the salt-processed Psoraleae Fructus group showed the presence of five RMs of psoralen/isopsoralen, two adducts with GSH, and one adduct with cysteine. In addition, 10 proteins modified by the RMs of psoralen/isopsoralen were identified. A total of 133 differential proteins were detected in the livers of mice in the salt-processed Psoraleae Fructus group, including 92 with up-regulated expression and 41 with down-regulated expression. These differential proteins mainly involved ribosomes, rRNAs, and glutathione, affecting the proteasome pathway. The qPCR results were consistent with the differential proteins. These findings suggest that the RMs of psoralen/isopsoralen can covalently bind to GSH and modify cysteine and lysine residues of liver proteins. This covalent modification of proteins by harmful substances can potentially result in liver damage. Therefore, it can be inferred that the oxidative stress damage induced by salt-processed Psoraleae Fructus may be associated with the abnormality of proteasome and its complex, biosynthesis of ribosomes and their nucleoprotein complex, rRNA binding, and glutathione binding.

Quantitative Characterization of the Impact of Protein-Protein Interactions on Ligand-Protein Binding: A Multi-Chain Dynamics Perturbation Analysis Method.

Many protein-protein interactions (PPIs) affect the ways in which small molecules bind to their constituent proteins, which can impact drug efficacy and regulatory mechanisms. While recent advances have improved our ability to independently predict both PPIs and ligand-protein interactions (LPIs), a comprehensive understanding of how PPIs affect LPIs is still lacking. Here, we examined 63 pairs of ligand-protein complexes in a benchmark dataset for protein-protein docking studies and quantified six typical effects of PPIs on LPIs. A multi-chain dynamics perturbation analysis method, called mcDPA, was developed to model these effects and used to predict small-molecule binding regions in protein-protein complexes. Our results illustrated that the mcDPA can capture the impact of PPI on LPI to varying degrees, with six similar changes in its predicted ligand-binding region. The calculations showed that 52% of the examined complexes had prediction accuracy at or above 50%, and 55% of the predictions had a recall of not less than 50%. When applied to 33 FDA-approved protein-protein-complex-targeting drugs, these numbers improved to 60% and 57% for the same accuracy and recall rates, respectively. The method developed in this study may help to design drug-target interactions in complex environments, such as in the case of protein-protein interactions.

Plug-and-play protein biosensors using aptamer-regulated in vitro transcription.

Molecular biosensors that accurately measure protein concentrations without external equipment are critical for solving numerous problems in diagnostics and therapeutics. Modularly transducing the binding of protein antibodies, protein switches or aptamers into a useful output remains challenging. Here, we develop a biosensing platform based on aptamer-regulated transcription in which aptamers integrated into transcription templates serve as inputs to molecular circuits that can be programmed to a produce a variety of responses. We modularly design molecular biosensors using this platform by swapping aptamer domains for specific proteins and downstream domains that encode different RNA transcripts. By coupling aptamer-regulated transcription with diverse transduction circuits, we rapidly construct analog protein biosensors and digital protein biosensors with detection ranges that can be tuned over two orders of magnitude and can exceed the binding affinity of the aptamer. Aptamer-regulated transcription is a straightforward and inexpensive approach for constructing programmable protein biosensors that could have diverse applications in research and biotechnology.

Maximal Genetic Code Symmetry Is a Physicochemical Purine-Pyrimidine Symmetry Language for Transcription and Translation in the Flow of Genetic Information from DNA to Proteins.

Until now, research has not taken into consideration the physicochemical purine-pyrimidine symmetries of the genetic code in the transcription and translation processes of proteinogenesis. Our Supersymmetry Genetic Code table, developed in 2022, is common and unique for all RNA and DNA living species. Its basic structure is a purine-pyrimidine symmetry net with double mirror symmetry. Accordingly, the symmetry of the genetic code directly shows its organisation based on the principle of nucleotide Watson-Crick and codon-anticodon pairing. The maximal purine-pyrimidine symmetries of codons show that each codon has a strictly defined and unchangeable position within the genetic code. We discovered that the physicochemical symmetries of the genetic code play a fundamental role in recognising and differentiating codons from mRNA and the anticodon tRNA and aminoacyl-tRNA synthetases in the transcription and translation processes. These symmetries also support the wobble hypothesis with non-Watson-Crick pairing interactions between the translation process from mRNA to tRNA. The Supersymmetry Genetic Code table shows a specific arrangement of the second base of codons, according to which it is possible that an anticodon from tRNA recognises whether a codon from mRNA belongs to an amino acid with two or four codons, which is very important in the purposeful use of the wobble pairing process. Therefore, we show that canonical and wobble pairings essentially do not lead to misreading and errors during translation, and we point out the role of physicochemical purine-pyrimidine symmetries in decreasing disorder according to error minimisation and preserving the integrity of biological processes during proteinogenesis.

Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics.

Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence have revolutionized our understanding of protein dynamics. Nuclear magnetic resonance spectroscopy provides atomic-resolution insights, while molecular dynamics simulations offer detailed trajectories of protein motions. Computational methods applied to X-ray crystallography and cryo-electron microscopy (cryo-EM) have enabled the exploration of protein dynamics, capturing conformational ensembles that were previously unattainable. The integration of machine learning, exemplified by AlphaFold2, has accelerated structure prediction and dynamics analysis. These approaches have revealed the importance of protein dynamics in allosteric regulation, enzyme catalysis, and intrinsically disordered proteins. The shift towards ensemble representations of protein structures and the application of single-molecule techniques have further enhanced our ability to capture the dynamic nature of proteins. Understanding protein dynamics is essential for elucidating biological mechanisms, designing drugs, and developing novel biocatalysts, marking a significant paradigm shift in structural biology and drug discovery.

The Impact of ARMS2 (rs10490924), VEGFA (rs3024997), TNFRSF1B (rs1061622), TNFRSF1A (rs4149576), and IL1B1 (rs1143623) Polymorphisms and Serum Levels on Age-Related Macular Degeneration Development and Therapeutic Responses.

Age-related macular degeneration (AMD) is a major global health problem as it is the leading cause of irreversible loss of central vision in the aging population. Anti-vascular endothelial growth factor (anti-VEGF) therapies are effective but do not respond optimally in all patients. This study investigates the genetic factors associated with susceptibility to AMD and response to treatment, focusing on key polymorphisms in the ARMS2 (rs10490924), IL1B1 (rs1143623), TNFRSF1B (rs1061622), TNFRSF1A (rs4149576), VEGFA (rs3024997), ARMS2, IL1B1, TNFRSF1B, TNFRSF1A, and VEGFA serum levels in AMD development and treatment efficacy. This study examined the associations of specific genetic polymorphisms and serum protein levels with exudative and early AMD and the response to anti-VEGF treatment. The AA genotype of VEGFA (rs3024997) was significantly associated with a 20-fold reduction in the odds of exudative AMD compared to the GG + GA genotypes. Conversely, the TT genotype of ARMS2 (rs10490924) was linked to a 4.2-fold increase in the odds of exudative AMD compared to GG + GT genotypes. In females, each T allele of ARMS2 increased the odds by 2.3-fold, while in males, the TT genotype was associated with a 5-fold increase. Lower serum IL1B levels were observed in the exudative AMD group compared to the controls. Early AMD patients had higher serum TNFRSF1B levels than controls, particularly those with the GG genotype of TNFRSF1B rs1061622. Exudative AMD patients with the CC genotype of TNFRSF1A rs4149576 had lower serum TNFRSF1A levels compared to the controls. Visual acuity (VA) analysis showed that non-responders had better baseline VA than responders but experienced decreased VA after treatment, whereas responders showed improvement. Central retinal thickness (CRT) reduced significantly in responders after treatment and was lower in responders compared to non-responders after treatment. The T allele of TNFRSF1B rs1061622 was associated with a better response to anti-VEGF treatment under both dominant and additive genetic models. These findings highlight significant genetic and biochemical markers associated with AMD and treatment response. This study found that the VEGFA rs3024997 AA genotype reduces the odds of exudative AMD, while the ARMS2 rs10490924 TT genotype increases it. Lower serum IL1B levels and variations in TNFRSF1B and TNFRSF1A levels were linked to AMD. The TNFRSF1B rs1061622 T allele was associated with better anti-VEGF treatment response. These markers could potentially guide risk assessment and personalized treatment for AMD.

Identification of protein partners for small molecules reshapes the understanding of nonalcoholic steatohepatitis and drug discovery.

AIMS: Nonalcoholic steatohepatitis (NASH) is the severe subtype of nonalcoholic fatty diseases (NAFLD) with few options for treatment. Patients with NASH exhibit partial responses to the current therapeutics and adverse effects. Identification of the binding proteins for the drugs is essential to understanding the mechanism and adverse effects of the drugs and fuels the discovery of potent and safe drugs. This paper aims to critically discuss recent advances in covalent and noncovalent approaches for identifying binding proteins that mediate NASH progression, along with an in-depth analysis of the mechanisms by which these targets regulate NASH. MATERIALS AND METHODS: A literature search was conducted to identify the relevant studies in the database of PubMed and the American Chemical Society. The search covered articles published from January 1990 to July 2024, using the search terms with keywords such as NASH, benzophenone, diazirine, photo-affinity labeling, thermal protein profiling, CETSA, target identification. KEY FINDINGS: The covalent approaches utilize drugs modified with diazirine and benzophenone to covalently crosslink with the target proteins, which facilitates the purification and identification of target proteins. In addition, they map the binding sites in the target proteins. By contrast, noncovalent approaches identify the binding targets of unmodified drugs in the intact cell proteome. The advantages and limitations of both approaches have been compared, along with a comprehensive analysis of recent innovations that further enhance the efficiency and specificity. SIGNIFICANCE: The analyses of the applicability of these approaches provide novel tools to delineate NASH pathogenesis and promote drug discovery.

An expanded molecular and systems toolbox for imaging, mapping, and controlling local translation.

Localized protein translation occurs through trafficking of mRNAs and protein translation machineries to different compartments of the cell, leading to rapid on-site synthesis of proteins in response to signaling cues. The spatiotemporally precise nature of the local translation process necessitates continual developments of technologies reviewed herein to visualize and map biomolecular components and the translation process with better spatial and temporal resolution and with fewer artifacts. We also discuss approaches to control local translation, which can serve as a design paradigm for subcellular genetic devices for eukaryotic synthetic biology.

VPS13C and STING expression in neuropsychiatric systemic lupus erythematosus: unveiling an unbreached territory.

OBJECTIVES: To measure the expression level of the vacuolar protein sorting 13 (VPS13) gene and stimulator of interferon genes (STING) in patients with SLE with and without reported neuropsychiatric symptoms to establish their possible role in the pathogenesis of neuropsychiatric SLE (NPSLE). METHODS: This study included 100 subjects: 50 patients diagnosed with SLE and 50 age-matched and sex-matched healthy participants as the control group. The patients with SLE were further subdivided into NPSLE and non-NPSLE groups. All the subjects underwent rheumatological, neurological and psychological evaluation, MRI, VPS13C gene and STING expression assessment via quantitative real-time PCR. RESULTS: Seventy-eight per cent of the SLE group were classified as non-NPSLE, and 22% were classified as NPSLE. Positive MRI results were found in 55% of the patients with NPSLE and 7.7% of the patients without NPSLE.VPS13C expression levels were decreased in the patients with SLE compared with the control (p<0.001), while STING expression levels showed higher levels in the patients in comparison with the control (p<0.001). Both markers showed significant differences between the MRI-positive and MRI-negative groups.At a cut-off value of 0.225 for the VPS13C assessment and a cut-off value of 3.15 for STING expression, both markers were able to distinguish patients with NPSLE from those who were non-NPSLE; however, VPS13C performed better. CONCLUSION: The VPS13C expression levels were decreased in patients with NPSLE compared with patients without NPSLE, while STING expression levels showed higher levels in NPSLE. Both were associated with the MRI findings. To distinguish patients with NPSLE from those without it, the VPS13C assessment performed better.

Microplastic Contamination in Aquafeed Ingredients Used as Protein and Carbohydrate Sources.

The current study aimed to evaluate the occurrence of microplastics in feed ingredients commonly used as protein and carbohydrate (energy) sources to understand and mitigate microplastic contamination. Microplastics average was 1.27, 0.69, 2.85, 0.55, 0.07, and 0.17 particle g- 1 in fishmeal, soybean meal, poultry by-products, rice bran, wheat bran, and wheat flour, respectively. Notably, poultry by-products demonstrated significantly higher microplastic levels than other ingredients (p < 0.05). The dominant microplastic shape was microfibers, with prevalent sizes ranging from 500 to 1000 µm. We estimated that packaging materials are a significant source of pollution due to the high presence of polypropylene and other polymers. Top aquaculture species with the greatest microplastic exposure risk include the Catla catla, Hypophthalmichthys nobilis, and Oreochromis niloticus. This research extends our knowledge of microplastic pathways, contributes to improving aquafeed quality, and provides the basis for determining the risk of microplastic exposure in aquafeed.

The improvement mechanism of volatile for cooked Tibetan pork assisted with ultrasound at low-temperature: Based on the differences in oxidation of lipid and protein.

Low-temperature cooking causes flavor weakness while improving the texture and digestive properties of meat. To enhance the flavor of low-temperature cooked Tibetan pork, samples were cooked at low-temperature with or without ultrasound-assisted (UBTP, BTP) for different times (30 min, 90 min) and then analyzed using GC-MS and LC-MS. The results showed that ultrasound-assisted cooking caused a significant increase in lipid oxidation by 9.10% in the early stage of the treatment. Additionally, at the later stage of ultrasound-assisted processing, proteins were oxidized and degraded, which resulted in a remarkable rise in the protein carbonyl content by 6.84%. With prolonged effects of ultrasound and low-temperature cooking, the formation of phenylacetaldehyde in UBTP-90 sample originated from the degradation of phenylalanine through multivariate statistics and correlation analysis. Meanwhile, trans, cis-2,6-nonadienal and 1-octen-3-one originated from the degradation of linolenic acid and arachidonic acid. This study clarified the mechanism of ultrasound-assisted treatment improving the flavor of low-temperature-cooked Tibetan pork based on the perspective of lipids and proteins oxidation, providing theoretical supports for flavor enhancement in Tibetan pork-related products.

Multi-pass, single-molecule nanopore reading of long protein strands.

The ability to sequence single protein molecules in their native, full-length form would enable a more comprehensive understanding of proteomic diversity. Current technologies, however, are limited in achieving this goal1,2. Here, we establish a method for the long-range, single-molecule reading of intact protein strands on a commercial nanopore sensor array. By using the ClpX unfoldase to ratchet proteins through a CsgG nanopore3,4, we provide single-molecule evidence that ClpX translocates substrates in two-residue steps. This mechanism achieves sensitivity to single amino acids on synthetic protein strands hundreds of amino acids in length, enabling the sequencing of combinations of single-amino-acid substitutions and the mapping of post-translational modifications, such as phosphorylation. To enhance classification accuracy further, we demonstrate the ability to reread individual protein molecules multiple times, and we explore the potential for highly accurate protein barcode sequencing. Furthermore, we develop a biophysical model that can simulate raw nanopore signals a priori on the basis of residue volume and charge, enhancing the interpretation of raw signal data. Finally, we apply these methods to examine full-length, folded protein domains for complete end-to-end analysis. These results provide proof of concept for a platform that has the potential to identify and characterize full-length proteoforms at single-molecule resolution.

Deep generative models of protein structure uncover distant relationships across a continuous fold space.

Our views of fold space implicitly rest upon many assumptions that impact how we analyze, interpret and understand protein structure, function and evolution. For instance, is there an optimal granularity in viewing protein structural similarities (e.g., architecture, topology or some other level)? Similarly, the discrete/continuous dichotomy of fold space is central, but remains unresolved. Discrete views of fold space bin similar folds into distinct, non-overlapping groups; unfortunately, such binning can miss remote relationships. While hierarchical systems like CATH are indispensable resources, less heuristic and more conceptually flexible approaches could enable more nuanced explorations of fold space. Building upon an Urfold model of protein structure, here we present a deep generative modeling framework, termed DeepUrfold, for analyzing protein relationships at scale. DeepUrfold's learned embeddings occupy high-dimensional latent spaces that can be distilled for a given protein in terms of an amalgamated representation uniting sequence, structure and biophysical properties. This approach is structure-guided, versus being purely structure-based, and DeepUrfold learns representations that, in a sense, define superfamilies. Deploying DeepUrfold with CATH reveals evolutionarily-remote relationships that evade existing methodologies, and suggests a mostly-continuous view of fold space-a view that extends beyond simple geometric similarity, towards the realm of integrated sequence â†” structure â†” function properties.

Characterization of the AGR2-NPM3 axis uncovers the AGR2 involvement in PD-L1 regulation in colorectal cancer.

Despite extensive research, the molecular role of AGR2 in the progression and metastasis of colorectal cancer (CRC) has not been fully characterized. We used quantitative mass spectrometry (SWATH MS) to identify differentially expressed proteins in paired CRC cell models of the SW480 and SW620 cell lines in response to AGR2 protein level manipulation. Relying on the results from SWATH MS and subsequent immunochemical validation, we selected NMP3 as the top candidate protein associated with AGR2 in CRC tumour cells in our screen. RT‒qPCR and immunochemical analysis confirmed the involvement of AGR2-mediated regulation of NPM3 at the transcriptional and posttranscriptional levels. Since PD-L1 is a constituent of the NPM3 regulatory axis, we aimed to correlate the changes in PD-L1 to the differential expression of AGR2 in our cell models. We found that AGR2 positively regulates PD-L1 levels in both SW480 and SW620 cell lines; additionally, several different CRC patient transcriptome cohorts confirmed the association of AGR2 with PD-L1. Our work reveals a new AGR2-NPM3 regulatory axis and the involvement of AGR2 in the regulation of PD-L1, which paves the way for the association of AGR2 with immune evasion in CRC cells.

DTI-LM: language model powered drug-target interaction prediction.

MOTIVATION: The identification and understanding of drug-target interactions (DTIs) play a pivotal role in the drug discovery and development process. Sequence representations of drugs and proteins in computational model offer advantages such as their widespread availability, easier input quality control, and reduced computational resource requirements. These make them an efficient and accessible tools for various computational biology and drug discovery applications. Many sequence-based DTI prediction methods have been developed over the years. Despite the advancement in methodology, cold start DTI prediction involving unknown drug or protein remains a challenging task, particularly for sequence-based models. Introducing DTI-LM, a novel framework leveraging advanced pretrained language models, we harness their exceptional context-capturing abilities along with neighborhood information to predict DTIs. DTI-LM is specifically designed to rely solely on sequence representations for drugs and proteins, aiming to bridge the gap between warm start and cold start predictions. RESULTS: Large-scale experiments on four datasets show that DTI-LM can achieve state-of-the-art performance on DTI predictions. Notably, it excels in overcoming the common challenges faced by sequence-based models in cold start predictions for proteins, yielding impressive results. The incorporation of neighborhood information through a graph attention network further enhances prediction accuracy. Nevertheless, a disparity persists between cold start predictions for proteins and drugs. A detailed examination of DTI-LM reveals that language models exhibit contrasting capabilities in capturing similarities between drugs and proteins. AVAILABILITY AND IMPLEMENTATION: Source code is available at: https://github.com/compbiolabucf/DTI-LM.

Protein Visualizer 2.0: Intuitive and Interactive Visualization of Protein Topology and Co/Post-Translational Modifications.

Topology and post-translational modifications (PTMs) are critical features in studying the structures and functions of proteins. Several popular and detailed protein visualization algorithms are available for such studies, such as PyMOL, NCBI's iCn3D, UniProt PTM viewing, and Protter. However, none of the studies depict the special relationships between key structural features such as N-glycosylation, disulfide bonds, potential N-glycosylation sites (free sequons), potential disulfide bonds (free cysteine sites), and protein topology in a way conducive to detailed analyses. We introduce Protein Visualizer 2.0 (https://sfu-sun-lab.github.io/protein-visualizer-2.0/), a web-based tool that visualizes these features and the topology of all human proteins. This tool allows users to readily assess potential conflicts among predicted protein topology and known co/post-translational modifications. This tool also helps to reveal hidden relationships among displayed structural features.

Identification of Tunable, Environmentally Responsive Fluorogenic Dyes by High-Throughput Screening.

Small molecule dyes remain essential biological tools, yet only a handful of environmentally responsive fluorogenic small molecules are available for routine characterization of protein state. Here, we report the development and execution of a high throughput screen to identify compounds that increase in fluorescence in response to binding of lipophilic sites of proteins. This effort yielded two small molecules that potently indicate the presence of a range of common proteins and outperform common dyes in differential scanning fluorimetry experiments. Structure activity relationship studies revealed that these two scaffolds can be tuned both for their quantum yields and emission wavelengths. This work affords a straightforward framework for the discovery of new fluorophores and adds two fluorogenic probes to the toolbox for studying protein state.

PGBind: pocket-guided explicit attention learning for protein-ligand docking.

As more and more protein structures are discovered, blind protein-ligand docking will play an important role in drug discovery because it can predict protein-ligand complex conformation without pocket information on the target proteins. Recently, deep learning-based methods have made significant advancements in blind protein-ligand docking, but their protein features are suboptimal because they do not fully consider the difference between potential pocket regions and non-pocket regions in protein feature extraction. In this work, we propose a pocket-guided strategy for guiding the ligand to dock to potential docking regions on a protein. To this end, we design a plug-and-play module to enhance the protein features, which can be directly incorporated into existing deep learning-based blind docking methods. The proposed module first estimates potential pocket regions on the target protein and then leverages a pocket-guided attention mechanism to enhance the protein features. Experiments are conducted on integrating our method with EquiBind and FABind, and the results show that their blind-docking performances are both significantly improved and new start-of-the-art performance is achieved by integration with FABind.