A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction.

Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exploring the nature of disease. However, accurately identifying protein-ligand binding sites remains a challenging task. To address this, we propose PGpocket, a geometric deep learning-based framework to improve protein-ligand binding site prediction. Initially, the protein surface is converted into a point cloud, and then the geometric and chemical properties of each point are calculated. Subsequently, the point cloud graph is constructed based on the inter-point distances, and the point cloud graph neural network (GNN) is applied to extract and analyze the protein surface information to predict potential binding sites. PGpocket is trained on the scPDB dataset, and its performance is verified on two independent test sets, Coach420 and HOLO4K. The results show that PGpocket achieves a 58% success rate on the Coach420 dataset and a 56% success rate on the HOLO4K dataset. These results surpass competing algorithms, demonstrating PGpocket's advancement and practicality for protein-ligand binding site prediction.

Ligand Binding Mechanisms in Human Cone Visual Pigments.

Vertebrate vision starts with light absorption by visual pigments in rod and cone photoreceptor cells of the retina. Rhodopsin, in rod cells, responds to dim light, whereas three types of cone opsins (red, green, and blue) function under bright light and mediate color vision. Cone opsins regenerate with retinal much faster than rhodopsin, but the molecular mechanism of regeneration is still unclear. Recent advances in the area pinpoint transient intermediate opsin conformations, and a possible secondary retinal-binding site, as determinant factors for regeneration. In this Review, we compile previous and recent findings to discuss possible mechanisms of ligand entry in cone opsins, involving a secondary binding site, which may have relevant functional and evolutionary implications.

Addressing data imbalance problems in ligand-binding site prediction using a variational autoencoder and a convolutional neural network.

Since 2015, a fast growing number of deep learning-based methods have been proposed for protein-ligand binding site prediction and many have achieved promising performance. These methods, however, neglect the imbalanced nature of binding site prediction problems. Traditional data-based approaches for handling data imbalance employ linear interpolation of minority class samples. Such approaches may not be fully exploited by deep neural networks on downstream tasks. We present a novel technique for balancing input classes by developing a deep neural network-based variational autoencoder (VAE) that aims to learn important attributes of the minority classes concerning nonlinear combinations. After learning, the trained VAE was used to generate new minority class samples that were later added to the original data to create a balanced dataset. Finally, a convolutional neural network was used for classification, for which we assumed that the nonlinearity could be fully integrated. As a case study, we applied our method to the identification of FAD- and FMN-binding sites of electron transport proteins. Compared with the best classifiers that use traditional machine learning algorithms, our models obtained a great improvement on sensitivity while maintaining similar or higher levels of accuracy and specificity. We also demonstrate that our method is better than other data imbalance handling techniques, such as SMOTE, ADASYN, and class weight adjustment. Additionally, our models also outperform existing predictors in predicting the same binding types. Our method is general and can be applied to other data types for prediction problems with moderate-to-heavy data imbalances.

Exploring the composition of protein-ligand binding sites for cancerous inhibitor of PP2A (CIP2A) by inhibitor guided binding analysis: paving a new way for the Discovery of drug candidates against triple negative breast cancer (TNBC).

Triple-negative breast cancer (TNBC) is associated with high-grade invasive carcinoma leading to a 10% to 15% death rate in younger premenopausal women. Targeting cancerous inhibitors of protein phosphatase (CIP2A) has been a highly effective approach for exploring therapeutic drug candidates. Lapatinib, a dual tyrosine kinase inhibitor, has shown promising inhibition properties by inducing apoptosis in TNBC carcinogenesis in vivo. Despite knowledge of the 3D structure of CIP2A, no reports provide insight into CIP2A ligand binding sites. To this effect, we conducted in silico site identification guided by lapatinib binding. Four of the five sites identified were cross-validated, and the stem domain revealed more excellent ligand binding affinity. The binding affinity of lapatinib in these sites was further computed using the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) approach. According to MM/PBSA//200 ns MD simulations, lapatinib exhibited a higher binding affinity against CIP2A in site 2 with ΔG critical values of -37.1 kcal/mol. The steadiness and tightness of lapatinib with CIP2A inside the stem domain disclosed glutamic acid-318 as the culprit amino acid with the highest electrostatic energy. These results provide clear information on the CIP2A domain capable of ligand binding and validate lapatinib as a promising CIP2A inhibitor in TNBC carcinogenesis.

Structural and Druggability Landscape of Frizzled G Protein-Coupled Receptors.

Class Frizzled G protein-coupled receptors (GPCRs), which includes the Smoothened receptor (SMO) and 10 Frizzled receptors (FZDs), are responsible for mediating fundamental signaling in embryonic development and tissue homeostasis. Dysregulation of these receptors can lead to cancer. Structural understanding of these molecules has provided insight to their function and signaling, and guided drug discovery. To date, the structures of the multi- and individual domains of SMO, 14 FZD extracellular domains, and the transmembrane domain (TMD) of FZD4, have been reported. Here, we review all reported frizzled family structures and diverse signalosome models, with an emphasis on the different ligand binding sites and lipid binding grooves, aiming to uncover the druggability landscape of the frizzled GPCR family.

The X-ray crystal structure of human A15C neuroglobin reveals both native/de novo disulfide bonds and unexpected ligand-binding sites.

Human neuroglobin (Ngb) contains a heme group and three Cys residues (Cys46, Cys55, and Cys120) in the polypeptide chain. By introducing an additional Cys at position 15, the X-ray structure of A15C Ngb mutant was solved at a high resolution of 1.35 Å, which reveals the formation of both the native (C46C55) and the engineered (C15C120) disulfide bonds, likely playing a functional and structural role, respectively, according to the geometry analysis. Unexpectedly, 1,4-dioxane from the crystallization reagents was bound not only to the protein surface, but also to the heme distal pocket, providing insights into protein-ligand interactions for the globin and guiding the design of functional heme enzymes.

Consensus mutagenesis and computational simulation provide insight into the desaturation catalytic mechanism for delta 6 fatty acid desaturase.

Fatty acid desaturase (FADS) generates double bond at a certain position of the corresponding polyunsaturated fatty acids (PUFAs) with high selectivity, the enzyme activity and PUFAs products of which are essential to biological systems and are associated with a variety of physiological diseases. Little is known about the structure of FADSs and their amino acid residues related to catalytic activities. Identifying key residues of Micromonas pusilla delta 6 desaturase (MpFADS6) provides a point of departure for a better understanding of desaturation. In this study, conserved amino acids were anchored through gene consensus analysis, thereby generating corresponding variants by site-directed mutagenesis. To achieve stable and high-efficiency expression of MpFADS6 and its variants in Saccharomyces cerevisiae, the key points of induced expression were optimized. The contribution of conserved residues to the function of enzyme was determined by analyzing enzyme activity of the variants. Molecular modeling indicated that these residues are essential to catalytic activities, or substrate binding. Mutants MpFADS6[Q409R] and MpFADS6[M242P] abolished desaturation, while MpFADS6[F419V] and MpFADS6[A374Q] significantly reduced catalytic activities. Given that certain residues have been identified to have a significant impact on MpFADS6 activities, it is put forward that histidine-conserved region III of FADS6 is related to electronic transfer during desaturation, while histidine-conserved regions I and II are related to desaturation. These findings provide new insights and methods to determine the structure, mechanism and directed transformation of membrane-bound desaturases.

Actin binding to galectin-13/placental protein-13 occurs independently of the galectin canonical ligand-binding site.

The gene for galectin-13 (Gal-13, placental protein 13) is only present in primates, and its low expression level in maternal serum may promote preeclampsia. In the present study, we used pull-down experiments and biolayer interferometry to assess the interaction between Gal-13 and actin. These studies uncovered that human Gal-13 (hGal-13) and Saimiri boliviensis boliviensis (sGal-13) strongly bind to α- and ß-/γ-actin, with Ca2+ and adenosine triphosphate, significantly enhancing the interactions. This in turn suggests that h/sGal-13 may inhibit myosin-induced contraction when vascular smooth muscle cells undergo polarization. Here, we solved the crystal structure of sGal-13 bound to lactose and found that it exists as a monomer in contrast to hGal-13 which is a dimer. The distribution of sGal-13 in HeLa cells is similar to that of hGal-13, indicating that monomeric Gal-13 is the primary form in cells. Even though sGal-13 binds to actin, hGal-13 ligand-binding site mutants do not influence hGal-13/actin binding, whereas the monomeric mutant C136S/C138S binds to actin more strongly than the wild-type hGal-13. Overall, our study demonstrates that monomeric Gal-13 binds to actin, an interaction that is independent of the galectin canonical ligand-binding site.

Distinctive communication networks in inactive states of ß2 -adrenergic receptor: Mutual information and entropy transfer analysis.

Mutual information and entropy transfer analysis employed on two inactive states of human beta-2 adrenergic receptor (ß2 -AR) unraveled distinct communication pathways. Previously, a so-called "highly" inactive state of the receptor was observed during 1.5 microsecond long molecular dynamics simulation where the largest intracellular loop (ICL3) was swiftly packed onto the G-protein binding cavity, becoming entirely inaccessible. Mutual information quantifying the degree of correspondence between backbone-Cα fluctuations was mostly shared between intra- and extra-cellular loop regions in the original inactive state, but shifted to entirely different regions in this latest inactive state. Interestingly, the largest amount of mutual information was always shared among the mobile regions. Irrespective of the conformational state, polar residues always contributed more to mutual information than hydrophobic residues, and also the number of polar-polar residue pairs shared the highest degree of mutual information compared to those incorporating hydrophobic residues. Entropy transfer, quantifying the correspondence between backbone-Cα fluctuations at different timesteps, revealed a distinctive pathway directed from the extracellular site toward intracellular portions in this recently exposed inactive state for which the direction of information flow was the reverse of that observed in the original inactive state where the mobile ICL3 and its intracellular surroundings drove the future fluctuations of extracellular regions.

NewBG: A surrogate corticosteroid-binding globulin with an unprecedentedly high ligand release efficacy.

The introduction of ligand-binding sites into proteins and the engineering of molecular allosteric coupling pathways are topical issues in protein design. Here, we show that these issues can be addressed concurrently, using the serpin human α1-antichymotrypsin (ACT) as a model. We have introduced up to 15 amino acid substitutions into ACT, converting it into a surrogate corticosteroid-binding globulin (CBG), thereby creating a new binding globulin (NewBG). Human CBG and ACT share 46% sequence identity, and CBG served as the blue-print for our design, which was guided by side-chain-packing calculations, ITC measurements and crystal structure determinations. Upon transfer of ligand-interacting residues from CBG to ACT and mutation of specific second shell residues, a NewBG variant was obtained, which binds cortisol with 1.5 µM affinity. This novel serpin (NewBG-III) binds cortisol with a 33-fold lower affinity than CBG, but shares a similar ligand-binding profile and binding mode when probed with different steroid ligands and site-directed mutagenesis. An additional substitution, i.e. A349R, created NewBG-III-allo, which introduced an allosteric coupling between ligand binding and the serpin-like S-to-R transition in ACT. In NewBG-III-allo, the proteinase-triggered S-to-R transition leads to a greater than 200-fold reduction in ligand affinity, and crystal structures suggest that this is mediated by the L55V and A349R substitutions. This reduction significantly exceeds the 10-fold reduction in binding affinity observed in human CBG.

Introducing "best single template" models as reference baseline for the Continuous Automated Model Evaluation (CAMEO).

Critical blind assessment of structure prediction techniques is crucial for the scientific community to establish the state of the art, identify bottlenecks, and guide future developments. In Critical Assessment of Techniques in Structure Prediction (CASP), human experts assess the performance of participating methods in relation to the difficulty of the prediction task in a biennial experiment on approximately 100 targets. Yet, the development of automated computational modeling methods requires more frequent evaluation cycles and larger sets of data. The "Continuous Automated Model EvaluatiOn (CAMEO)" platform complements CASP by conducting fully automated blind prediction evaluations based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the Protein Data Bank (PDB). Each week, CAMEO publishes benchmarking results for predictions corresponding to a set of about 20 targets collected during a 4-day prediction window. CAMEO benchmarking data are generated consistently for all methods at the same point in time, enabling developers to cross-validate their method's performance, and referring to their results in publications. Many successful participants of CASP have used CAMEO-either by directly benchmarking their methods within the system or by comparing their own performance to CAMEO reference data. CAMEO offers a variety of scores reflecting different aspects of structure modeling, for example, binding site accuracy, homo-oligomer interface quality, or accuracy of local model confidence estimates. By introducing the "bestSingleTemplate" method based on structure superpositions as a reference for the accuracy of 3D modeling predictions, CAMEO facilitates objective comparison of techniques and fosters the development of advanced methods.

Three-Dimensional Krawtchouk Descriptors for Protein Local Surface Shape Comparison.

Direct comparison of three-dimensional (3D) objects is computationally expensive due to the need for translation, rotation, and scaling of the objects to evaluate their similarity. In applications of 3D object comparison, often identifying specific local regions of objects is of particular interest. We have recently developed a set of 2D moment invariants based on discrete orthogonal Krawtchouk polynomials for comparison of local image patches. In this work, we extend them to 3D and construct 3D Krawtchouk descriptors (3DKDs) that are invariant under translation, rotation, and scaling. The new descriptors have the ability to extract local features of a 3D surface from any region-of-interest. This property enables comparison of two arbitrary local surface regions from different 3D objects. We present the new formulation of 3DKDs and apply it to the local shape comparison of protein surfaces in order to predict ligand molecules that bind to query proteins.

Characterization of the High-Affinity Drug Ligand Binding Site of Mouse Recombinant TSPO.

The optimization of translocator protein (TSPO) ligands for Positron Emission Tomography as well as for the modulation of neurosteroids is a critical necessity for the development of TSPO-based diagnostics and therapeutics of neuropsychiatrics and neurodegenerative disorders. Structural hints on the interaction site and ligand binding mechanism are essential for the development of efficient TSPO ligands. Recently published atomic structures of recombinant mammalian and bacterial TSPO1, bound with either the high-affinity drug ligand PK 11195 or protoporphyrin IX, have revealed the membrane protein topology and the ligand binding pocket. The ligand is surrounded by amino acids from the five transmembrane helices as well as the cytosolic loops. However, the precise mechanism of ligand binding remains unknown. Previous biochemical studies had suggested that ligand selectivity and binding was governed by these loops. We performed site-directed mutagenesis to further test this hypothesis and measured the binding affinities. We show that aromatic residues (Y34 and F100) from the cytosolic loops contribute to PK 11195 access to its binding site. Limited proteolytic digestion, circular dichroism and solution two-dimensional (2-D) NMR using selective amino acid labelling provide information on the intramolecular flexibility and conformational changes in the TSPO structure upon PK 11195 binding. We also discuss the differences in the PK 11195 binding affinities and the primary structure between TSPO (TSPO1) and its paralogous gene product TSPO2.

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12.

Every second year, the community experiment "Critical Assessment of Techniques for Structure Prediction" (CASP) is conducting an independent blind assessment of structure prediction methods, providing a framework for comparing the performance of different approaches and discussing the latest developments in the field. Yet, developers of automated computational modeling methods clearly benefit from more frequent evaluations based on larger sets of data. The "Continuous Automated Model EvaluatiOn (CAMEO)" platform complements the CASP experiment by conducting fully automated blind prediction assessments based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the PDB Protein Data Bank. CAMEO publishes weekly benchmarking results based on models collected during a 4-day prediction window, on average assessing ca. 100 targets during a time frame of 5 weeks. CAMEO benchmarking data is generated consistently for all participating methods at the same point in time, enabling developers to benchmark and cross-validate their method's performance, and directly refer to the benchmarking results in publications. In order to facilitate server development and promote shorter release cycles, CAMEO sends weekly email with submission statistics and low performance warnings. Many participants of CASP have successfully employed CAMEO when preparing their methods for upcoming community experiments. CAMEO offers a variety of scores to allow benchmarking diverse aspects of structure prediction methods. By introducing new scoring schemes, CAMEO facilitates new development in areas of active research, for example, modeling quaternary structure, complexes, or ligand binding sites.

Structural basis for differential activities of enantiomeric PPARγ agonists: Binding of S35 to the alternate site.

Peroxisome proliferator-activated receptor γ (PPARγ) is a member of the nuclear receptor superfamily. It functions as a ligand-activated transcription factor and plays important roles in the regulation of adipocyte differentiation, type 2 diabetes mellitus, and inflammation. Many PPARγ agonists bind to the canonical ligand-binding pocket near the activation function-2 (AF-2) helix (i.e., helix H12) of the ligand-binding domain (LBD). More recently, an alternate ligand-binding site was identified in PPARγ LBD; it is located beside the Ω loop between the helices H2' and H3. We reported previously that the chirality of two optimized enantiomeric PPARγ ligands (S35 and R35) differentiates their PPARγ transcriptional activity, binding affinity, and inhibitory activity toward Cdk5 (cyclin-dependent kinase 5)-mediated phosphorylation of PPARγ at Ser245 (in PPARγ1 numbering; Ser273 in PPARγ2 numbering). S35 is a PPARγ phosphorylation inhibitor with promising glucose uptake potential, whereas R35 behaves as a potent conventional PPARγ agonist. To provide a structural basis for understanding the differential activities of these enantiomeric ligands, we have determined crystal structures of the PPARγ LBD in complex with either S35 or R35. S35 and R35 bind to the PPARγ LBD in significantly different manners. The partial agonist S35 occupies the alternate site near the Ω loop, whereas the full agonist R35 binds entirely to the canonical LBP. Alternate site binding of S35 affects the PPARγ transactivation and the inhibitory effect on PPARγ Ser245 phosphorylation. This study provides a useful platform for the development of a new generation of PPARγ ligands as anti-diabetic drug candidates.

Protein ligand-binding site comparison by a reduced vector representation derived from multidimensional scaling of generalized description of binding sites.

Proteins serve various functions in living cells. When they exert their functions, physical contact with other molecules occurs. A close connection therefore exists between their functions and structures. Therefore, comparison and classification about known and predicted protein structures provides important insight into the structural features of proteins, elucidating their functions and structures. Analyzing the mutual interactions between proteins and small molecules is important to predict the ligands which bind to parts of putative ligand binding sites. Such analysis demands a fast and efficient method for comparing ligand binding sites because of the recent increase of protein structure information. A method has been developed for representing a ligand binding site with one reduced vector for binding site comparison. Using our method, one can calculate the similarity between ligand binding sites merely by calculating the inner product of 11-dimensional vectors. The method explained herein shows higher performance of the similarity between binding sites than metrics used in existing alignment-free methods. It also shows performance that is comparable to accurate methods developed recently, which employ solving the optimization problem: APoc. Moreover, these study results suggest that this new method can provide similarities faster than our previous method.

Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.

Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein-ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein-ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein-ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems.

Intracellular accumulation of structurally varied isothiocyanates correlates with inhibition of nitric oxide production in proinflammatory stimuli-activated tumorigenic macrophage-like cells.

In the present study, we analyzed the intracellular accumulation of 6-(methylsulfinyl)hexyl isothiocyanate (6MITC) and its analogs in proinflammatory stimuli-activated J774.1 cells to predict the biological potencies of the ITCs. Our present analyses exhibited that the intracellular accumulation was in the order of 6MITC>2b>2e≈2c>2g>2d>2f>2h. Investigation of reactivity of the ITCs with glutathione (GSH) in the tumor cells revealed partial inhibition of GSH by the ITCs. Furthermore, the inhibition of nitric oxide (NO) production in the tumor cells was ascribed to the intracellularly accumulated ITCs. The NO suppression was correlated with the inhibition of tumor cell growth. Our present results suggest that the intracellular accumulation of the ITCs can be used to predict their biological potencies, such as inhibition of NO production that was correlated with suppression of tumor cell growth. To the best of our knowledge, this is the first report to predict the biological potency of 6MITC and its analogs with their intracellular accumulation.

CRD: A de novo design algorithm for the prediction of cognate protein receptors for small molecule ligands.

While predicting a ligand that binds to a protein is feasible with current methods, the opposite, i.e., the prediction of a receptor for a ligand remains challenging. We present an approach for predicting receptors of a given ligand that uses de novo design and structural bioinformatics. We have developed the algorithm CRD, comprising multiple modules combining fragment-based sub-site finding, a machine learning function to estimate the size of the site, a genetic algorithm that encodes knowledge on protein structures and a physics-based fitness scoring scheme. CRD includes a pseudo-receptor design component followed by a mapping component to identify proteins that might contain these sites. CRD recovers the sites and receptors of several natural ligands. It designs similar sites for similar ligands, yet to some extent can distinguish between closely related ligands. CRD correctly predicts receptor classes for several drugs and might become a valuable tool for drug discovery.

Genomic analysis of Salmonella bacteriophages revealed multiple endolysin ORFs and importance of ligand-binding site of receptor-binding protein.

Salmonella is a prevalent foodborne pathogen causing millions of global cases annually. Antimicrobial resistance is a growing public health concern, leading to search for alternatives like bacteriophages. A total of 97 bacteriophages, isolated from cattle farms (n = 48), poultry farms (n = 37), and wastewater (n = 5) samples in Türkiye, were subjected to host-range analysis using 36 Salmonella isolates with 18 different serotypes. The broadest host range belonged to an Infantis phage (MET P1-091), lysing 28 hosts. A total of 10 phages with the widest host range underwent further analysis, revealing seven unique genomes (32-243 kb), including a jumbophage (>200 kb). Except for one with lysogenic properties, none of them harbored virulence or antibiotic resistance genes, making them potential Salmonella reducers in different environments. Examining open reading frames (ORFs) of endolysin enzymes revealed surprising findings: five of seven unique genomes contained multiple endolysin ORFs. Despite sharing same endolysin sequences, phages exhibited significant differences in host range. Detailed analysis unveiled diverse receptor-binding protein sequences, with similar structures but distinct ligand-binding sites. These findings emphasize the importance of ligand-binding sites of receptor-binding proteins. Additionally, bacterial reduction curve and virulence index revealed that Enteritidis phages inhibit bacterial growth even at low concentrations, unlike Infantis and Kentucky phages.