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We have developed a deep learning based method to decode the protein-ligand interactions and predict their probability of binding. We use 3-Dimensional structure of proteins and ligand molecules with the ligand docked to the receptor and use the information at the interaction region to assess the binding of the protein-ligand complex.
We created a cloud-based application for running AlphaFold2 and related structural programs called Foldy. Foldy is built on Helm / Kubernetes, which enables a facile production deployment and a scalable backend. Once set up by an institution, Foldy requires no software expertise from its users. It can be used to predict the structure of large proteins (up to 3000 amino acids), to visualize Pfam and antiSMASH annotations, and to perform ligand docking with Autodock Vina.
Publicly available collections of drug-like molecules have grown to comprise tens of billions of compounds due to advances in combinatorial chemistry. Traditional methods for identifying "hit" molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have the major drawback that they require exceptional computing capabilities for even relatively small collections of molecules. Hyperdimensional Computing (HDC) is a recently-proposed learning paradigm that represents data with high-dimension binary vectors; this allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas. We consider existing HDC approaches for molecular property classification and introduce two novel encodings of a commonly-used molecular representation, the extended connectivity fingerprint (ECFP). We show that HDC-based inference methods are as much as 91 times more efficient than traditional machine learning methods, and achieve an acceleration of nearly nine orders of magnitude compared to molecular docking. Our results show that HDC accelerated methods retain competitive accuracy on a number of well-studied tasks such as molecular property predictions using the MoleculeNet dataset, and bind/no-bind activity classification using the DUD-E and LIT-PCBA datasets. Our work thus motivates further investigation into molecular representation learning to develop ultraefficient pre-screening tools.
EGAN-3F (Equivariant Graph Attention Network - 3D Conformers & Feature Fusion) presents an innovative approach for predicting binding affinity between small molecules and protein targets, a fundamental task in drug discovery. Traditional structure-based methods often depend on protein-ligand complex structures obtained from crystallography or molecular docking. In contrast, ligand-only machine learning models using 1D or 2D representations such as SMILES have been developed to predict binding affinity without structural information about the target; however, their accuracy is often limited due to the lack of 3D ligand information. EGAN-3F addresses this limitation by integrating spatially aware graph learning with traditional descriptor-based features. We systematically investigate how combining 2D and 3D molecular representations enhances binding affinity prediction from SMILES strings. This approach underscores the importance of modeling conformational diversity and incorporating chemically meaningful descriptors to improve predictive accuracy. The key innovation of EGAN-3F lies in its ability to achieve robust ligand-based binding affinity predictions without requiring protein-ligand complex structures, effectively bridging the gap between purely structural and ligand-only modeling paradigms.
To withstand the growing demand of commodity volume and its strain on the transportation infrastructure, it is necessary to identify the flow of commodities by route and mode. However, a national multimodal freight routing model does not exist for the U.S. The development of such model requires multiple building blocks, such as virtual representations of roadway, railway, and waterway networks, transload facilities (TFs), and access/egress links. Most of these blocks have a robust database in the U.S., except for the TFs. Here, this paper presents the fusion of dispersed and heterogeneous representations of multimodal TFs into a single, comprehensive, geospatial freight TF dataset. The TF dataset is derived from several sources, including the U.S. Army Corps of Engineers Master Docks Plus, the National Transportation Atlas Database, the Intermodal Association of North America, industry publications, and other public information. First, individual datasets were queried and reconciled. A geocoding/reverse geocoding process was applied to get the best street address and latitude/longitude location for each terminal. Then, duplicate terminals were identified by a fuzzy match algorithm based on terminal name and location, and removed. Validation was performed by visual inspection of random facilities. The main contributions of this work are: a publicly available version of the TF dataset, including facility location and multimodal transfer capability of 9,003 facilities, and an enterprise-version with the same facilities but including commodity handling capabilities. The main purpose of developing the TF dataset is to inform multimodal routing algorithms. The proposed TF dataset allows for credibly modeling the multimodal transfer of commodities within shipment routes.
The development of pathogenic antibody inhibitors against coagulation factor VIII (FVIII) occurs in ~30% of congenital hemophilia A patients receiving FVIII replacement therapy as well as in all cases of acquired hemophilia A. KM33 is an anti-C1 domain antibody inhibitor previously isolated from a severe hemophilia A patient. In addition to potently blocking FVIII binding to von Willebrand factor and phospholipid surfaces, KM33 disrupts FVIII binding to lipoprotein receptor-related protein 1 (LRP1), which drives FVIII hepatic clearance and antigen presentation in dendritic cells. Here, we report on the structure of FVIII bound to NB33, a recombinant derivative of KM33, by single-particle cryo-electron microscopy. Furthermore, structural analysis reveals the NB33 epitope localizes to FVIII residues R2090-S2094 and I2158-R2159 which constitute membrane-binding loops in the C1 domain. Further analysis reveals multiple FVIII lysine and arginine residues, previously shown to mediate binding to LRP1, dock onto an acidic cleft at the NB33 variable domain interface, thus blocking a putative LRP1 binding site. Together, these results demonstrate a novel mechanism of FVIII inhibition by a patient-derived antibody inhibitor and provide structural evidence toward engineering FVIII with reduced LRP1-mediated clearance.
Abstract The intrinsic tenase (Xase) complex, formed by factors (f) VIIIa and fIXa, forms on activated platelet surfaces and catalyzes the activation of factor X to Xa, stimulating thrombin production in the blood coagulation cascade. The structural organization of the membrane-bound Xase complex remains largely unknown, hindering our understanding of the structural underpinnings that guide Xase complex assembly. Here, we aimed to characterize the Xase complex bound to a lipid nanodisc with biolayer interferometry (BLI), Michaelis–Menten kinetics, and small-angle X-ray scattering (SAXS). Using immobilized lipid nanodiscs, we measured binding rates and nanomolar affinities for fVIIIa, fIXa, and the Xase complex. Enzyme kinetic measurements demonstrated the assembly of an active enzyme complex in the presence of lipid nanodiscs. An ab initio molecular envelope of the nanodisc-bound Xase complex allowed us to computationally model fVIIIa and fIXa docked onto a flexible lipid membrane and identify protein–protein interactions. Our results highlight multiple points of contact between fVIIIa and fIXa, including a novel interaction with fIXa at the fVIIIa A1–A3 domain interface. Lastly, we identified hemophilia A/B-related mutations with varying severities at the fVIIIa/fIXa interface that may regulate Xase complex assembly. Together, our results support the use of SAXS as an emergent tool to investigate the membrane-bound Xase complex and illustrate how mutations at the fVIIIa/fIXa dimer interface may disrupt or stabilize the activated enzyme complex.
Background: The carbapenem subclass of β-lactams is among the most potent antibiotics available today. Emerging evidence shows that, unlike other subclasses of β-lactams, carbapenems bind to and inhibit non-classical transpeptidases (L,D-transpeptidases) that generate 3 → 3 linkages in bacterial peptidoglycan. The carbapenems biapenem and tebipenem exhibit therapeutically valuable potencies against Mycobacterium tuberculosis (Mtb). Results: Here, we report the X-ray crystal structures of Mtb L,D-transpeptidase-2 (Ldt Mt2 ) complexed with biapenem or tebipenem. Despite significant variations in carbapenem sulfur side chains, biapenem and tebipenem ultimately form an identical adduct that docks to the outer cavity of Ldt Mt2 . We propose that this common adduct is an enzyme catalyzed decomposition of the carbapenem adduct by a mechanism similar to S-conjugate elimination by β-lyases. Conclusion: The results presented here demonstrate biapenem and tebipenem bind to the outer cavity of Ldt Mt2 , covalently inactivate the enzyme, and subsequently degrade via an S-conjugate elimination mechanism. We discuss structure based drug design based on the findings and propose that the S-conjugate elimination can be leveraged to design novel agents to deliver and locally release antimicrobial factors to act synergistically with the carbapenem carrier.
Trichoderma reesei is an organism extensively used in the bioethanol industry, owing to its capability to produce enzymes capable of breaking down holocellulose into simple sugars. The uptake of carbohydrates generated from cellulose breakdown is crucial to induce the signaling cascade that triggers cellulase production. However, the sugar transporters involved in this process in T. reesei remain poorly identified and characterized. To address this gap, this study used temporal membrane proteomics analysis to identify five known and nine putative sugar transporters that may be involved in cellulose degradation by T. reesei. Docking analysis pointed out potential ligands for the putative sugar transporter Tr44175. Further functional validation of this transporter was carried out in Saccharomyces cerevisiae. The results showed that Tr44175 transports a variety of sugar molecules, including cellobiose, cellotriose, cellotetraose, and sophorose. This study has unveiled a transporter Tr44175 capable of transporting cellobiose, cellotriose, cellotetraose, and sophorose. Our study represents the first inventory of T. reesei sugar transportome once exposed to cellulose, offering promising potential targets for strain engineering in the context of bioethanol production.
Aromatase is a member of the cytochrome P450 superfamily responsible for a key step in the biosynthesis of estrogens. As estrogens are involved in the control of important reproduction-related processes, including sexual diferentiation and maturation, aromatase is a potential target for endocrine disrupting chemicals as well as breast cancer therapy. In this work, 3D-QSAR combined with quantitative profle of protein–ligand interactions was employed in the identifcation and characterization of critical steric and electronic features of aromatase-inhibitor complexes and the estimation of their quantitative contribution to inhibition potency. Bioactivity data on pIC 50 values of 175 steroidal and 124 azaheterocyclic human aromatase inhibitors (AIs) were used for the 3D-QSAR analysis. For the quantitative description of the efects of the hydrophobic contact and nitrogen–heme–iron coordination on aromatase inhibition, the hydrophobicity density feld model and the smallest dual descriptor Δf(r) S were introduced, respectively. The model revealed that hydrophobic contact and nitrogen–heme–iron coordination primarily determines inhibition potency of steroidal and azaheterocyclic AIs, respectively. Moreover, hydrogen bonds with key amino acid residues, in particular Asp309 and Met375, and interaction with the heme–iron are required for potent inhibition. Phe221 and Thr310 appear to be quite fexible and adopt diferent conformations according to a substituent at 4- or 6-position of steroids. Flexible docking results indicate that proper representation of the residues’ fexibility is critical for reasonable description of binding of the structurally diverse inhibitors. Our results provide a quantitative and mechanistic understanding of inhibitory activity of steroidal and azaheterocyclic AIs of relevance to adverse outcome pathway development and rational drug design.
The possibility that G protein-coupled receptor family C member A (GPRC6A) is the osteocalcin (Ocn)-sensing G protein-coupled receptor that directly regulates pancreatic β-cell functions is controversial. In the current study, we found that Ocn and an Ocn-derived C-terminal hexapeptide directly activate GPRC6A-dependent ERK signaling in vitro. Computational models probe the structural basis of Ocn binding to GPRC6A and predict that the C-terminal hexapeptide docks to the extracellular side of the transmembrane domain of GPRC6A. Consistent with the modeling, mutations in the computationally identified binding pocket of GPRC6A reduced Ocn and C-terminal hexapeptide activation of this receptor. In addition, selective deletion of Gprc6a in β-cells (Gprc6a β-cell-cko ) by crossing Gprc6a flox/flox mice with Ins2-Cre mice resulted in reduced pancreatic weight, islet number, insulin protein content, and insulin message expression. Both islet size and β-cell proliferation were reduced in Gprc6a β-cell-cko compared with control mice. Gprc6a β-cell-cko exhibited abnormal glucose tolerance, but normal insulin sensitivity. Islets isolated from Gprc6a β-cell-cko mice showed reduced insulin simulation index in response to Ocn. Here, these data establish the structural basis for Ocn direct activation of GPRC6A and confirm a role for GPRC6A in regulating β-cell proliferation and insulin secretion.
Background: Experience with application of a robotic surgery platform in the management of breast cancer is limited. The preliminary results of the robotic nipple-sparing mastectomy (R-NSM) and immediate breast reconstruction (IBR) with Gel implant procedure are reported. Methods: The medical records of patients from a single institution who underwent an R-NSM and IBR with Gel implant procedure for breast cancer during the period March 2017 to February 2018 were assessed. Data on clinicopathologic characteristics, type of surgery, complications, and recurrence were analyzed to determine the effectiveness and oncologic safety of R-NSM. Patient-reported cosmetic outcome results were obtained. Results: A total of 22 patients who received 23 R-NSM and IBR with Gel implant procedures were analyzed. The mean operation time for R-NSM was 118.8 ± 50.6 min, and 74.5 ± 26.6 min for Gel implant reconstruction. Docking time quickly dropped from 20 to 6–8 min, and the time needed to complete R-NSM was usually completed within 100 min after accumulation of case experience. Mean blood loss was 37 ± 38.2 mL, and the positive surgical margin rate was 0%. Three (13%) patients had transit nipple ischemia change, and no total nipple-areolar complex necrosis cases were observed. No local recurrence or mortality was found during a mean 6.9 ± 3.5 months of follow-up. All 22 patients were satisfied with the postoperative aesthetic outcome. Conclusion: From our preliminary experience, R-NSM and IBR with Gel implant is a safe procedure, with good cosmetic results, and could be a promising new technique for breast cancer patients indicated for mastectomy.
Aptamers can be highly specific for their targets, which implies precise molecular recognition between aptamer and target. However, as small polymers, their structures are more subject to environmental conditions than the more constrained longer RNAs such as those that constitute the ribosome. To understand the balance between structural and environmental factors in establishing ligand specificity of aptamers, we examined the RNA aptamer (NEO1A) previously reported as specific for neomycin-B. We show that NEO1A can recognize other aminoglycosides with similar affinities as for neomycin-B and its aminoglycoside specificity is strongly influenced by ionic strength and buffer composition. NMR and 2-aminopurine (2AP) fluorescence studies of the aptamer identified a flexible pentaloop and a stable binding pocket. Consistent with a well structured binding pocket, docking analysis results correlated with experimental measures of the binding energy for most ligands. Steady state fluorescence studies of 2AP-substituted aptamers confirmed that A16 moves to a more solvent accessible position upon ligand binding while A14 moves to a less solvent accessible position, which is most likely a base stack. Analysis of binding affinities of NEO1A sequence variants showed that the base in position 16 interacts differently with each ligand and the interaction is a function of the buffer constituents. Our results show that the pentaloop provides NEO1A with the ability to adapt to external influences on its structure, with the critical base at position 16 adjusting to incorporate each ligand into a stable pocket by hydrophobic interactions and/or hydrogen bonds depending on the ligand and the ionic environment.
mRNA capping enzymes are directed to nascent RNA polymerase II (Pol2) transcripts via interactions with the carboxy-terminal domains (CTDs) of Pol2 and transcription elongation factor Spt5. Fission yeast RNA triphosphatase binds to the Spt5 CTD, comprising a tandem repeat of nonapeptide motif TPAWNSGSK. Here we report the crystal structure of a Pct1·Spt5-CTD complex, which revealed two CTD docking sites on the Pct1 homodimer that engage TPAWN segments of the motif. Each Spt5 CTD interface, composed of elements from both subunits of the homodimer, is dominated by van der Waals contacts from Pct1 to the tryptophan of the CTD. The bound CTD adopts a distinctive conformation in which the peptide backbone makes a tight U-turn so that the proline stacks over the tryptophan. We show that Pct1 binding to Spt5 CTD is antagonized by threonine phosphorylation. Our results fortify an emerging concept of an “Spt5 CTD code” in which (i) the Spt5 CTD is structurally plastic and can adopt different conformations that are templated by particular cellular Spt5 CTD receptor proteins; and (ii) threonine phosphorylation of the Spt5 CTD repeat inscribes a binary on–off switch that is read by diverse CTD receptors, each in its own distinctive manner.
Histone tails play an important role in nucleosome structure and dynamics. Here we investigate the effect of truncation of histone tails H3, H4, H2A and H2B on nucleosome structure with 100 ns all-atom molecular dynamics simulations. Tail domains of H3 and H2B show propensity of α-helics formation during the intact nucleosome simulation. On truncation of H4 or H2B tails no structural change occurs in histones. However, H3 or H2A tail truncation results in structural alterations in the histone core domain, and in both the cases the structural change occurs in the H2Aα3 domain. We also find that the contacts between the histone H2A C terminal docking domain and surrounding residues are destabilized upon H3 tail truncation. The relation between the present observations and corresponding experiments is discussed.
The actin cytoskeleton is a dynamic structure that coordinates numerous fundamental processes in eukaryotic cells. Dozens of actin-binding proteins are known to be involved in the regulation of actin filament organization or turnover and many of these are stimulus-response regulators of phospholipid signaling. One of these proteins is the heterodimeric actin-capping protein (CP) which binds the barbed end of actin filaments with high affinity and inhibits both addition and loss of actin monomers at this end. The ability of CP to bind filaments is regulated by signaling phospholipids, which inhibit the activity of CP; however, the exact mechanism of this regulation and the residues on CP responsible for lipid interactions is not fully resolved. Here, we focus on the interaction of CP with two signaling phospholipids, phosphatidic acid (PA) and phosphatidylinositol (4,5)-bisphosphate (PIP2). Using different methods of computational biology such as homology modeling, molecular docking and coarse-grained molecular dynamics, we uncovered specific modes of high affinity interaction between membranes containing PA/phosphatidylcholine (PC) and plant CP, as well as between PIP 2 /PC and animal CP. In particular, we identified differences in the binding of membrane lipids by animal and plant CP, explaining previously published experimental results. Furthermore, we pinpoint the critical importance of the C-terminal part of plant CPα subunit for CP–membrane interactions. We prepared a GST-fusion protein for the C-terminal domain of plant α subunit and verified this hypothesis with lipid-binding assays in vitro.
Foldy is a cloud-based application that allows non-computational biologists to easily utilize advanced AI-based structural biology tools, including AlphaFold and DiffDock. With many deployment options, it can be employed by individuals, labs, universities, and companies in the cloud without requiring hardware resources, but it can also be configured to utilize locally available computers. Foldy enables scientists to predict the structure of proteins and complexes up to 6000 amino acids with AlphaFold, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. In our manuscript, we detail Foldy’s interface design, deployment strategies, and optimization for various user scenarios. We demonstrate its application through case studies including rational enzyme design and analyzing proteins with domains of unknown function. Furthermore, we compare Foldy’s interface and management capabilities with other open and closed source tools in the field, illustrating its practicality in managing complex data and computation tasks. Our manuscript underlines the benefits of Foldy as a day-to-day tool for life science researchers, and shows how Foldy can make modern tools more accessible and efficient.
Canonical ubiquitin-like proteins (UBLs) such as ubiquitin, Sumo, NEDD8, and ISG15 are ligated to targets by E1-E2-E3 multienzyme cascades. The Sumo cascade, conserved among all eukaryotes, regulates numerous biological processes including protein localization, transcription, DNA replication, and mitosis. Sumo conjugation is initiated by the heterodimeric Aos1-Uba2 E1 enzyme (in humans called Sae1-Uba2), which activates Sumo's C-terminus, binds the dedicated E2 enzyme Ubc9, and promotes Sumo C-terminal transfer between the Uba2 and Ubc9 catalytic cysteines. To gain insights into details of E1-E2 interactions in the Sumo pathway, we determined crystal structures of the C-terminal ubiquitin fold domain (ufd) from yeast Uba2 (Uba2{sup ufd}), alone and in complex with Ubc9. The overall structures of both yeast Uba2{sup ufd} and Ubc9 superimpose well on their individual human counterparts, suggesting conservation of fundamental features of Sumo conjugation. Docking the Uba2{sup ufd}-Ubc9 and prior full-length human Uba2 structures allows generation of models for steps in Sumo transfer from Uba2 to Ubc9, and supports the notion that Uba2 undergoes remarkable conformational changes during the reaction. Comparisons to previous structures from the NEDD8 cascade demonstrate that UBL cascades generally utilize some parallel E1-E2 interaction surfaces. In addition, the structure of the Uba2{sup ufd}-Ubc9 complex reveals interactions unique to Sumo E1 and E2. Comparison with a previous Ubc9-E3 complex structure demonstrates overlap between Uba2 and E3 binding sites on Ubc9, indicating that loading with Sumo and E3-catalyzed transfer to substrates are strictly separate steps. The results suggest mechanisms establishing specificity and order in Sumo conjugation cascades.