The utility of geometrical and chemical restraint information extracted from predicted ligand-binding sites in protein structure refinement

Exhaustive exploration of molecular interactions at the level of complete proteomes requires efficient and reliable computational approaches to protein function inference. Ligand docking and ranking techniques show considerable promise in their ability to quantify the interactions between proteins and small molecules. Despite the advances in the development of docking approaches and scoring functions, the genome-wide application of many ligand docking/screening algorithms is limited by the quality of the binding sites in theoretical receptor models constructed by protein structure prediction. In this study, we describe a new template-based method for the local refinement of ligand-binding regions in protein models using remotely related templates identified by threading. We designed a Support Vector Regression (SVR) model that selects correct binding site geometries in a large ensemble of multiple receptor conformations. The SVR model employs several scoring functions that impose geometrical restraints on the Cα positions, account for the specific chemical environment within a binding site and optimize the interactions with putative ligands. The SVR score is well correlated with the RMSD from the native structure; in 47% (70%) of the cases, the Pearson\u27s correlation coefficient is \u3e0.5 (\u3e0.3). When applied to weakly homologous models, the average heavy atom, local RMSD from the native structure of the top-ranked (best of top five) binding site geometries is 3.1. Å (2.9. Å) for roughly half of the targets; this represents a 0.1 (0.3). Å average improvement over the original predicted structure. Focusing on the subset of strongly conserved residues, the average heavy atom RMSD is 2.6. Å (2.3. Å). Furthermore, we estimate the upper bound of template-based binding site refinement using only weakly related proteins to be ∼2.6. Å RMSD. This value also corresponds to the plasticity of the ligand-binding regions in distant homologues. The Binding Site Refinement (BSR) approach is available to the scientific community as a web server that can be accessed at http://cssb.biology.gatech.edu/bsr/. © 2010 Elsevier Inc

Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models

Molecular complexes formed by proteins and small-molecule ligands are ubiquitous, and predicting their 3D structures can facilitate both biological discoveries and the design of novel enzymes or drug molecules. Here we propose NeuralPLexer, a deep generative model framework to rapidly predict protein-ligand complex structures and their fluctuations using protein backbone template and molecular graph inputs. NeuralPLexer jointly samples protein and small-molecule 3D coordinates at an atomistic resolution through a generative model that incorporates biophysical constraints and inferred proximity information into a time-truncated diffusion process. The reverse-time generative diffusion process is learned by a novel stereochemistry-aware equivariant graph transformer that enables efficient, concurrent gradient field prediction for all heavy atoms in the protein-ligand complex. NeuralPLexer outperforms existing physics-based and learning-based methods on benchmarking problems including fixed-backbone blind protein-ligand docking and ligand-coupled binding site repacking. Moreover, we identify preliminary evidence that NeuralPLexer enriches bound-state-like protein structures when applied to systems where protein folding landscapes are significantly altered by the presence of ligands. Our results reveal that a data-driven approach can capture the structural cooperativity among protein and small-molecule entities, showing promise for the computational identification of novel drug targets and the end-to-end differentiable design of functional small-molecules and ligand-binding proteins

Simulating molecular docking with haptics

Intermolecular binding underlies various metabolic and regulatory processes of the cell, and the therapeutic and pharmacological properties of drugs. Molecular docking systems model and simulate these interactions in silico and allow the study of the binding process. In molecular docking, haptics enables the user to sense the interaction forces and intervene cognitively in the docking process. Haptics-assisted docking systems provide an immersive virtual docking environment where the user can interact with the molecules, feel the interaction forces using their sense of touch, identify visually the binding site, and guide the molecules to their binding pose. Despite a forty-year research e�ort however, the docking community has been slow to adopt this technology. Proprietary, unreleased software, expensive haptic hardware and limits on processing power are the main reasons for this. Another signi�cant factor is the size of the molecules simulated, limited to small molecules. The focus of the research described in this thesis is the development of an interactive haptics-assisted docking application that addresses the above issues, and enables the rigid docking of very large biomolecules and the study of the underlying interactions. Novel methods for computing the interaction forces of binding on the CPU and GPU, in real-time, have been developed. The force calculation methods proposed here overcome several computational limitations of previous approaches, such as precomputed force grids, and could potentially be used to model molecular exibility at haptic refresh rates. Methods for force scaling, multipoint collision response, and haptic navigation are also reported that address newfound issues, particular to the interactive docking of large systems, e.g. force stability at molecular collision. The i ii result is a haptics-assisted docking application, Haptimol RD, that runs on relatively inexpensive consumer level hardware, (i.e. there is no need for specialized/proprietary hardware)

Apollo Lightcraft Project

This second year of the NASA/USRA-sponsored Advanced Aeronautical Design effort focused on systems integration and analysis of the Apollo Lightcraft. This beam-powered, single-stage-to-orbit vehicle is envisioned as the shuttlecraft of the 21st century. The five person vehicle was inspired largely by the Apollo Command Module, then reconfigured to include a new front seat with dual cockpit controls for the pilot and co-pilot, while still retaining the 3-abreast crew accommodations in the rear seat. The gross liftoff mass is 5550 kg, of which 500 kg is the payload and 300 kg is the LH2 propellant. The round trip cost to orbit is projected to be three orders of magnitude lower than the current space shuttle orbiter. The advanced laser-driven 5-speed combined-cycle engine has shiftpoints at Mach 1, 5, 11 and 25+. The Apollo Lightcraft can climb into low Earth orbit in three minutes, or fly to any spot on the globe in less than 45 minutes. Detailed investigations of the Apollo Lightcraft Project this second year further evolved the propulsion system design, while focusing on the following areas: (1) man/machine interface; (2) flight control systems; (3) power beaming system architecture; (4) re-entry aerodynamics; (5) shroud structural dynamics; and (6) optimal trajectory analysis. The principal new findings are documented. Advanced design efforts for the next academic year (1988/1989) will center on a one meter+ diameter spacecraft: the Lightcraft Technology Demonstrator (LTD). Detailed engineering design and analyses, as well as critical proof-of-concept experiments, will be carried out on this small, near-term machine. As presently conceived, the LTD could be constructed using state of the art components derived from existing liquid chemical rocket engine technology, advanced composite materials, and high power laser optics

Highly Conserved Homotrimer Cavity Formed by the SARS-CoV-2 Spike Glycoprotein: A Novel Binding Site

An important stage in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) life cycle is the binding of the spike (S) protein to the angiotensin converting enzyme-2 (ACE2) host cell receptor. Therefore, to explore conserved features in spike protein dynamics and to identify potentially novel regions for drugging, we measured spike protein variability derived from 791 viral genomes and studied its properties by molecular dynamics (MD) simulation. The findings indicated that S2 subunit (heptad-repeat 1 (HR1), central helix (CH), and connector domain (CD) domains) showed low variability, low fluctuations in MD, and displayed a trimer cavity. By contrast, the receptor binding domain (RBD) domain, which is typically targeted in drug discovery programs, exhibits more sequence variability and flexibility. Interpretations from MD simulations suggest that the monomer form of spike protein is in constant motion showing transitions between an “up” and “down” state. In addition, the trimer cavity may function as a “bouncing spring” that may facilitate the homotrimer spike protein interactions with the ACE2 receptor. The feasibility of the trimer cavity as a potential drug target was examined by structure based virtual screening. Several hits were identified that have already been validated or suggested to inhibit the SARS-CoV-2 virus in published cell models. In particular, the data suggest an action mechanism for molecules including Chitosan and macrolides such as the mTOR (mammalian target of Rapamycin) pathway inhibitor Rapamycin. These findings identify a novel small molecule binding-site formed by the spike protein oligomer, that might assist in future drug discovery programs aimed at targeting the coronavirus (CoV) family of viruses

E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking

In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery. This work focuses on blind flexible selfdocking, where we aim to predict the positions, orientations and conformations of docked molecules. Traditional physics-based methods usually suffer from inaccurate scoring functions and high inference cost. Recently, data-driven methods based on deep learning techniques are attracting growing interest thanks to their efficiency during inference and promising performance. These methods usually either adopt a two-stage approach by first predicting the distances between proteins and ligands and then generating the final coordinates based on the predicted distances, or directly predicting the global roto-translation of ligands. In this paper, we take a different route. Inspired by the resounding success of AlphaFold2 for protein structure prediction, we propose E3Bind, an end-to-end equivariant network that iteratively updates the ligand pose. E3Bind models the protein-ligand interaction through careful consideration of the geometric constraints in docking and the local context of the binding site. Experiments on standard benchmark datasets demonstrate the superior performance of our end-to-end trainable model compared to traditional and recently-proposed deep learning methods.Comment: International Conference on Learning Representations (ICLR 2023

Structure Based Ligand Design for Monoamine Transporters and Mitogen Activated Kinase 5

Depression is a major psychological disorder that affects a person\u27s mental and physical abilities. The National Institute of Mental Health (NIMH) classified it as a serious medical illness. It causes huge economic, as well as financial impact on the people, and it is also becoming a major public health issue. Antidepressant drugs are prescribed to mitigate the suffering caused by this disorder. Different generations of antidepressants have been developed with dissimilar mechanisms of action. According to the Center for Disease Control, the usage of antidepressants has skyrocketed by 400 percent increase over 2005- 2008 survey period. This dramatic rise in usage indicates that these are the most prescribed drugs in the US. Even with the FDA mandated black box warning of increased suicidal thoughts upon use of selected antidepressants, these drugs are still being used at a higher rate. All classes of antidepressants are plagued by side effects with mainly sexual dysfunction common among them. To avoid the adverse effects, an emphasis is to discover novel structural drug scaffolds that can be further developed as a new generation of antidepressants. The importance of this research is to discover structurally novel antidepressants by performing in silico virtual screening (VS) of chemical databases using the serotonin transporter (SERT). In the absence of a SERT crystal structure, a homology model was developed. The homology model was utilized to develop the first structure-based pharmacophore for the extracellular facing secondary ligand binding pocket. The pharmacophore captured the necessary drug-SERT interaction pattern for SERT inhibitory action. This pharmacophore was employed as one of the filters for VS of candidate ligands. The ten compounds identified were purchased and tested pharmacologically. Out of the ten hits, three structurally novel ligands were identified as lead compounds. Two of these compounds exhibited selectivity towards SERT; the remaining lead compound was selective towards the dopamine transporter and displayed cocaine inhibition. The two SERT selective compounds will provide new opportunities in the development of novel therapeutics to treat depression. For dopamine transporter (DAT), the study was based on recently developed structurally diverse photo probes. In an effort to better understand the binding profile similarities among these different scaffolds, the photo probes were docked into DAT. The finger print analysis of the interaction pattern of docked poses was performed to identify the inhibitor-binding sites. For mitogen activated protein kinase 5 (MEK5), given the lack of structural information, a homology model of MEK5 was developed to guide the rational design of inhibitors. Docking of known MEK5 inhibitors into the homology model was performed to understand the inhibitory interaction profile. Several series of analogues were designed utilizing the generated interaction profile

Deorphanizing Human Cytochrome P450 Enzymes CYP4A22 and CYP4Z1 through Mechanistic in silico Modeling

Cytochrome P450 (CYP) enzymes are monooxygenases that catalyze the oxidation of structurally diverse substrates and are present in various lifeforms, including humans. Human CYPs catalyze the metabolism of xenobiotics including drugs and are involved in the essential biosynthesis of steroids, vitamins, and lipids. CYP-catalyzed metabolism and biosynthesis has been extensively studied recently, but several CYPs remain understudied despite their potential role in key biotransformation pathways. For these so-called ‘orphaned CYPs’, physiological function and structure are yet unknown, such as for CYP4A22 and 4Z1. CYP4A22 catalyzes the ω-hydroxylation of arachidonic acid to the angiogenic 20-hydroxyeicosatetraenoic acid. CYP4Z1 is overexpressed in breast cancer and other malignancies, which is correlated with tumor progression. Hence, CYP4Z1 is considered a promising breast cancer target that was not previously addressed by small molecule inhibitors. Here, we report our efforts to deorphanize CYP4A22 and 4Z1 together with our experimental partner Prof. Bureik. We were the first to predict the structure of CYP4A22 and 4Z1 by homology modeling and overcame the challenge of low-sequence similarity templates by incorporating substrate activities. We applied substrate docking and 3D pharmacophore modeling to rationalize how the binding site structure determines structure-activity relationships (SAR) trends. The well-known structural flexibility of CYPs was partially accounted for by molecular dynamics simulations. For the first time, enzyme-substrate interactions dynamics were analyzed with our novel dynamic pharmacophore approach, which led to the prediction of key residues. For CYP4A22, a residue influencing ω-hydroxylation (Phe320) and two binding residues (Arg96 and Arg233) were predicted. For CYP4Z1, the key role of Arg487 and assisting role of Asn381 for substrate binding were predicted, which was validated by in vitro mutational studies. The thereby validated CYP4Z1 model and substrate SAR were used in a virtual screening campaign resulting in a new potent and selective CYP4Z1 inhibitor (IC50: 63 ± 19 nM). Taken together, we established an in vitro/in silico deorphanization protocol that shed light on the structure-function relationships of CYP4A22 and 4Z1. This enabled us to discover a potent inhibitor of CYP4Z1 that will allow further studies on the physiological and pathophysiological role of the enzyme and might be further improved to target CYP4Z1 in a new therapeutical approach. Similar workflows could easily be applied to study other neglected enzymes in metabolism and other biotransformation pathways.Cytochrom P450 (CYP)-Enzyme sind Monooxygenasen, die die Oxidation strukturell diverser Substrate katalysieren und in verschiedenen Lebensformen, einschließlich des Menschen, vorkommen. Menschliche CYPs katalysieren den Metabolismus von Xenobiotika einschließlich Arzneistoffen und sind an der essenziellen Biosynthese von Steroiden, Vitaminen und Lipiden beteiligt. CYP-katalysierter Metabolismus und Biosynthese wurden in der Vergangenheit intensiv untersucht, aber einige CYPs sind trotz ihrer potenziellen Rolle in wichtigen Biotransformationswegen noch wenig erforscht. Für diese so genannten „orphaned“ oder „verwaisten“ CYPs, sind physiologische Funktion und Struktur noch unbekannt, wie z.B. CYP4A22 und 4Z1. CYP4A22 katalysiert die ω-Hydroxylierung von Arachidonsäure zu der angiogenen 20-Hydroxyeicosatetraensäure. CYP4Z1 wird bei Brustkrebs und anderen malignen Erkrankungen überexprimiert, was mit der Tumorprogression korreliert ist. Daher wird CYP4Z1 als ein vielversprechendes Brustkrebs-Target angesehen, das bisher nicht durch niedermolekulare Inhibitoren adressiert wurde. Hier berichten wir über unsere Bemühungen, CYP4A22 und 4Z1 zusammen mit unserem experimentellen Partner Prof. Bureik zu deorphanisieren. Wir waren die Ersten, die die Struktur von CYP4A22 und 4Z1 durch Homologiemodellierung vorhersagten und überwanden die Herausforderung der Templates mit geringer Sequenzähnlichkeit, indem wir Substrataktivitäten mit einbezogen. Wir wendeten Substrat-Docking und 3D-Pharmakophor-Modellierung an, um zu rationalisieren, wie die Struktur der Bindungstasche die Trends der Struktur-Aktivitäts-Beziehungen (SAR) bestimmt. Die bekannte strukturelle Flexibilität von CYPs wurde partiell durch Molekulardynamik-Simulationen berücksichtigt. Zum ersten Mal wurde die Dynamik der Enzym-Substrat-Interaktionen mit unserem neuartigen dynamischen Pharmakophor-Ansatz analysiert, was zur Vorhersage von wichtigen Aminosäuren führte. Für CYP4A22 wurde eine Aminosäure, die die ω-Hydroxylierung beeinflusst (Phe320) und zwei Bindungsaminosäuren (Arg96 und Arg233) vorhergesagt. Für CYP4Z1 wurde die Schlüsselrolle von Arg487 und die unterstützende Rolle von Asn381 für die Substratbindung vorhergesagt, welche durch in vitro Mutationsstudien validiert wurde. Das dadurch validierte CYP4Z1-Modell und die Substrat-SAR wurden in einer virtuellen Screening-Kampagne verwendet, die zu einen neuen potenten und selektiven CYP4Z1-Inhibitor führte (IC50: 63 ± 19 nM). Zusammengenommen haben wir ein in vitro/in silico Deorphanisierungsprotokoll etabliert, welches die Struktur-Funktionsbeziehungen von CYP4A22 und 4Z1 beleuchtet. Dies versetzte uns in die Lage einen potenten Inhibitor von CYP4Z1 zu entdecken, der weitere Studien über die physiologische und pathophysiologische Rolle des Enzyms ermöglichen wird und möglicherweise weiter verbessert werden kann, um CYP4Z1 in einem neuen therapeutischen Ansatz zu adressieren. Ähnliche Arbeitsabläufe könnte leicht angewendet werden, um andere vernachlässigte Enzyme im Metabolismus und anderen Biotransformationswegen zu untersuchen