Exploring the Potential of Structure-Based Deep Learning Approaches for T cell Receptor Design.

Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optimization of existing ones designed for specific functions, such as binding a target protein. Despite the demonstrated potential of such approaches in designing general protein binders, their application in designing immunotherapeutics remains relatively unexplored. A relevant application is the design of T cell receptors (TCRs). Given the crucial role of T cells in mediating immune responses, redirecting these cells to tumor or infected target cells through the engineering of TCRs has shown promising results in treating diseases, especially cancer. However, the computational design of TCR interactions presents challenges for current physics-based methods, particularly due to the unique natural characteristics of these interfaces, such as low affinity and cross-reactivity. For this reason, in this study, we explored the potential of two structure-based deep learning protein design methods, ProteinMPNN and ESM-IF, in designing fixed-backbone TCRs for binding target antigenic peptides presented by the MHC through different design scenarios. To evaluate TCR designs, we employed a comprehensive set of sequence- and structure-based metrics, highlighting the benefits of these methods in comparison to classical physics-based design methods and identifying deficiencies for improvement.

KVFinder-web: a web-based application for detecting and characterizing biomolecular cavities.

Molecular interactions that modulate catalytic processes occur mainly in cavities throughout the molecular surface. Such interactions occur with specific small molecules due to geometric and physicochemical complementarity with the receptor. In this scenario, we present KVFinder-web, an open-source web-based application of parKVFinder software for cavity detection and characterization of biomolecular structures. The KVFinder-web has two independent components: a RESTful web service and a web graphical portal. Our web service, KVFinder-web service, handles client requests, manages accepted jobs, and performs cavity detection and characterization on accepted jobs. Our graphical web portal, KVFinder-web portal, provides a simple and straightforward page for cavity analysis, which customizes detection parameters, submits jobs to the web service component, and displays cavities and characterizations. We provide a publicly available KVFinder-web at https://kvfinder-web.cnpem.br, running in a cloud environment as docker containers. Further, this deployment type allows KVFinder-web components to be configured locally and customized according to user demand. Hence, users may run jobs on a locally configured service or our public KVFinder-web.

First Nonphosphorylated Inhibitors of Phosphoglucose Isomerase Identified by Chemical Library Screening.

Human African trypanosomiasis, Chagas disease, and leishmaniasis are human infections caused by kinetoplastid parasites of the genera Trypanosoma and Leishmania. Besides their severity and global impact, treatments are still challenging. Currently available drugs have important limitations, highlighting the urgent need to develop new drugs. Phosphoglucose isomerase (PGI) is considered a promising target for the development of antiparasitic drugs, as it acts on two essential metabolic pathways, glycolysis and gluconeogenesis. Herein, we describe the identification of new nonphosphorylated inhibitors of Leishmania mexicana PGI ( LmPGI), with the potential for the development of antiparasitic drugs. A fluorescence-based high-throughput screening (HTS) assay was developed by coupling the activities of recombinant LmPGI with glucose-6-phosphate dehydrogenase and diaphorase. This coupled assay was used to screen 42,720 compounds from ChemBridge and TimTec commercial libraries. After confirmatory assays, selected LmPGI inhibitors were tested against homologous Trypanosoma cruzi and humans. The PGI hits are effective against trypanosomatid PGIs, with IC50 values in the micromolar range, and also against the human homologous enzyme. A computational analysis of cavities present on PGI's crystallographic structure suggests a potential binding site for the proposed mixed-type inhibition mechanism.

Improving predictions of protein-protein interfaces by combining amino acid-specific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages.

Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now integrated into the BlueStar STING suite of programs. Consequently, the prediction of protein-protein interfaces for all proteins available in the PDB is possible through STING_interfaces module, accessible at the following website: (http://www.cbi.cnptia.embrapa.br/SMS/predictions/index.html).

High resolution analysis of snake venom metalloproteinase (SVMP) peptide bond cleavage specificity using proteome based peptide libraries and mass spectrometry.

Both serine and metalloproteinases have been shown to play the role of toxins in the venoms of many snakes. Determination of the natural protein substrates of these toxins is an important feature in the toxinological characterization of these proteinases. Furthermore, characterization of their peptide bond specificity is of value for understanding active site preference of the proteinase associated with effective proteolysis as well as of use in the design of peptide substrates and inhibitor lead compounds. Typically the determination of peptide bond cleavage specificity of snake venom serine proteinases (SVSPs) and snake venom metalloproteinases (SVMPs) has been performed using limited sets of peptides or small oligopeptides as experimental substrates. Although this approach has yielded valuable data it is generally limited in scope due to the relatively small sets of substrates used to generate the consensus specificity sequences for these proteinases. In this study we use a large, plasma based, proteome-derived peptide library as substrates along with mass spectrometry to explore the peptide bond specificity of three PI SVMPs and one PIII SVMP to determine their individual peptide cleavage consensus sequences. All of the proteinases assayed displayed a clear preference for a leucine residue in the P1' site. Careful analysis of the specificity profiles of the SVMPs examined showed interesting differences in the preferences at the other P and P' sites suggesting functional differences between these proteinases. The PI SVMPs, leucurolysin-a, atrolysin C, and BaP1, showed preferences across the full P4 to P4' range whereas the PIII SVMP bothropasin showed a narrower range of preferences across the sites. In silico docking experiments with the experimentally derived consensus sequences as well as with comparison of the results to those in the literature regarding peptide bond specificity based on both peptide and protein substrates give rise to a fresh understanding of the specificity of these SVMPS and may serve as a foundation for future experiments to better elucidate their mechanism of action in the complex pathophysiology of snakebite envenomation.

High resolution analysis of snake venom metalloproteinase (SVMP) peptide bond cleavage specificity using proteome based peptide libraries and mass spectrometry. (AU)

Both serine and metalloproteinases have been shown to play the role of toxins in the venoms of many snakes. Determination of the natural protein substrates of these toxins is an important feature in the toxinological characterization of these proteinases. Furthermore, characterization of their peptide bond specificity is of value for understanding active site preference of the proteinase associated with effective proteolysis as well as of use in the design of peptide substrates and inhibitor lead compounds. Typically the determination of peptide bond cleavage specificity of snake venom serine proteinases (SVSPs) and snake venom metalloproteinases (SVMPs) has been performed using limited sets of peptides or small oligopeptides as experimental substrates. Although this approach has yielded valuable data it is generally limited in scope due to the relatively small sets of substrates used to generate the consensus specificity sequences for these proteinases. In this study we use a large, plasma based, proteome-derived peptide library as substrates along with mass spectrometry to explore the peptide bond specificity of three PI SVMPs and one PIII SVMP to determine their individual peptide cleavage consensus sequences. All of the proteinases assayed displayed a clear preference for a leucine residue in the P1Œ site. Careful analysis of the specificity profiles of the SVMPs examined showed interesting differences in the preferences at the other P and PŒ sites suggesting functional differences between these proteinases.

The origin of luciferase activity in Zophobas mealworm AMP/CoA-ligase (protoluciferase): luciferin stereoselectivity as a switch for the oxygenase activity.

Beetle luciferases evolved from AMP/CoA-ligases. However, it is unclear how the new luciferase activity evolved. In order to clarify this question, we compared the luminescence and catalytic properties of a recently cloned luciferase-like enzyme from Zophobas mealworm, an AMP/CoA-ligase displaying weak luminescence activity, with those of cloned luciferases from the three main families of luminescent beetles: Phrixthrix hirtus railroad worm; Pyrearinus termitilluminans click beetle and Photinus pyralis firefly. The catalytic constant of the mealworm enzyme was 2-4 orders of magnitude lower than that of beetle luciferases, but 3 orders of magnitude above the non-catalyzed chemiluminescence of luciferyl-adenylate in buffer. Studies with D- and L-luciferin and their adenylates show that the luminescence reaction of the luciferase-like enzyme and beetle luciferases are stereoselective for D-luciferin and its adenylate, and that the selectivity is determined mainly at the adenylation step. Modelling studies showed that the luciferin binding site cavity of this enzyme is smaller and more hydrophobic than that of beetle luciferases. Therefore Zophobas mealworm enzyme displays true luciferase activity, keeping the attributes of an ancient protoluciferase. These results suggest that stereoselectivity for D-luciferin may have been a key event for the origin of oxygenase/luciferase activity in AMP/CoA-ligases, and that efficient luciferase activity may have further evolved mainly by increasing the catalytic constant of the oxidative reaction and the quantum yield of bioluminescence.