ClusPro LigTBM: Automated Template-based Small Molecule Docking

Immunoinformatics-driven In silico vaccine design for Nipah virus (NPV): Integrating machine learning and computational epitope prediction

The Nipah virus (NPV) is a highly lethal virus, known for its significant fatality rate. The virus initially originated in Malaysia in 1998 and later led to outbreaks in nearby countries such as Bangladesh, Singapore, and India. Currently, there are no specific vaccines available for this virus. The current work employed the reverse vaccinology method to conduct a comprehensive analysis of the entire proteome of the NPV virus. The aim was to identify and choose the most promising antigenic proteins that could serve as potential candidates for vaccine development. We have also designed B and T cell epitopes-based vaccine candidate using immunoinformatics approach. We have identified a total of 5 novel Cytotoxic T Lymphocytes (CTL), 5 Helper T Lymphocytes (HTL), and 6 linear B-cell potential antigenic epitopes which are novel and can be used for further vaccine development against Nipah virus. Then we performed the physicochemical properties, antigenic, immunogenic and allergenicity prediction of the designed vaccine candidate against NPV. Further, Computational analysis indicated that these epitopes possessed highly antigenic properties and were capable of interacting with immune receptors. The designed vaccine were then docked with the human immune receptors, namely TLR-2 and TLR-4 showed robust interaction with the immune receptor. Molecular dynamics simulations demonstrated robust binding and good dynamics. After numerous dosages at varied intervals, computational immune response modeling showed that the immunogenic construct might elicit a significant immune response. In conclusion, the immunogenic construct shows promise in providing protection against NPV, However, further experimental validation is required before moving to clinical trials.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

DockOpt: A Tool for Automatic Optimization of Docking Models

Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.

Accurate ligand-protein docking in CASP15 using the ClusPro LigTBM server

In the ligand prediction category of CASP15, the challenge was to predict the positions and conformations of small molecules binding to proteins that were provided as amino acid sequences or as models generated by the AlphaFold2 program. For most targets, we used our template-based ligand docking program ClusPro ligTBM, also implemented as a public server available at https://ligtbm.cluspro.org/. Since many targets had multiple chains and a number of ligands, several templates, and some manual interventions were required. In a few cases, no templates were found, and we had to use direct docking using the Glide program. Nevertheless, ligTBM was shown to be a very useful tool, and by any ranking criteria, our group was ranked among the top five best-performing teams. In fact, all the best groups used template-based docking methods. Thus, it appears that the AlphaFold2-generated models, despite the high accuracy of the predicted backbone, have local differences from the x-ray structure that make the use of direct docking methods more challenging. The results of CASP15 confirm that this limitation can be frequently overcome by homology-based docking.

Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease

Background: We hypothesize that the poor survival outcomes of end-stage kidney disease (ESKD) patients undergoing hemodialysis are associated with a low filtering efficiency and selectivity. The current gold standard criteria using single or several markers show an inability to predict or disclose the treatment effect and disease progression accurately. Methods: We performed an integrated mass spectrometry-based metabolomic and proteomic workflow capable of detecting and quantifying circulating small molecules and proteins in the serum of ESKD patients. Markers linked to cardiovascular disease (CVD) were validated on human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. Results: We identified dozens of elevated molecules in the serum of patients compared with healthy controls. Surprisingly, many metabolites, including lipids, remained at an elevated blood concentration despite dialysis. These molecules and their associated physical interaction networks are correlated with clinical complications in chronic kidney disease. This study confirmed two uremic toxins associated with CVD, a major risk for patients with ESKD. Conclusion: The retained molecules and metabolite-protein interaction network address a knowledge gap of candidate uremic toxins associated with clinical complications in patients undergoing dialysis, providing mechanistic insights and potential drug discovery strategies for ESKD.

HydraMap v.2: Prediction of Hydration Sites and Desolvation Energy with Refined Statistical Potentials

The complex network of water molecules within the binding pocket of a target protein undergoes alterations upon ligand binding, presenting a significant challenge for conventional molecular modeling methods to accurately characterize and compute the associated energy changes. We have previously developed an empirical method, HydraMap (J. Chem. Inf. Model. 2020, 60, 4359-4375), which employs statistical potentials to predict hydration sites and compute desolvation energy, achieving a reasonable balance between accuracy and speed. In this work, we present its improved version, namely, HydraMap v.2. We updated the statistical potentials for protein-water interactions through an analysis of 17 042 crystal protein structures. We also introduced a new feature to evaluate ligand-water interactions by incorporating statistical potentials derived from the solvated structures of 9878 small organic molecules produced by molecular dynamics simulations. By combining these potentials, HydraMap v.2 can predict and compare the hydration sites in a binding pocket before and after ligand binding, identifying key water molecules involved in the binding process, such as those forming bridging hydrogen bonds and unstable ones that can be replaced. We demonstrated the application of HydraMap v.2 in explaining the structure-activity relationship of a panel of MCL-1 inhibitors. The desolvation energies calculated by summing the energy change of each hydration site before and after ligand binding showed good correlation with known ligand binding affinities on six target proteins. In conclusion, HydraMap v.2 offers a cost-effective solution for estimating the desolvation energy during protein-ligand binding and also is practical in guiding lead optimization in structure-based drug discovery.

High Accuracy Prediction of PROTAC Complex Structures

The design of PROteolysis-TArgeting Chimeras (PROTACs) requires bringing an E3 ligase into proximity with a target protein to modulate the concentration of the latter through its ubiquitination and degradation. Here, we present a method for generating high-accuracy structural models of E3 ligase-PROTAC-target protein ternary complexes. The method is dependent on two computational innovations: adding a "silent" convolution term to an efficient protein-protein docking program to eliminate protein poses that do not have acceptable linker conformations and clustering models of multiple PROTACs that use the same E3 ligase and target the same protein. Results show that the largest consensus clusters always have high predictive accuracy and that the ensemble of models can be used to predict the dissociation rate and cooperativity of the ternary complex that relate to the degrading activity of the PROTAC. The method is demonstrated by applications to known PROTAC structures and a blind test involving PROTACs against BRAF mutant V600E. The results confirm that PROTACs function by stabilizing a favorable interaction between the E3 ligase and the target protein but do not necessarily exploit the most energetically favorable geometry for interaction between the proteins.

Preclinical testing of dabigatran in trypsin-dependent pancreatitis

Pancreatitis, the inflammatory disorder of the pancreas, has no specific therapy. Genetic, biochemical, and animal model studies revealed that trypsin plays a central role in the onset and progression of pancreatitis. Here, we performed biochemical and preclinical mouse experiments to offer proof of concept that orally administered dabigatran etexilate can inhibit pancreatic trypsins and shows therapeutic efficacy in trypsin-dependent pancreatitis. We found that dabigatran competitively inhibited all human and mouse trypsin isoforms (Ki range 10-79 nM) and dabigatran plasma concentrations in mice given oral dabigatran etexilate well exceeded the Ki of trypsin inhibition. In the T7K24R trypsinogen mutant mouse model, a single oral gavage of dabigatran etexilate was effective against cerulein-induced progressive pancreatitis, with a high degree of histological normalization. In contrast, spontaneous pancreatitis in T7D23A mice, which carry a more aggressive trypsinogen mutation, was not ameliorated by dabigatran etexilate, given either as daily gavages or by mixing it with solid chow. Taken together, our observations showed that benzamidine derivatives such as dabigatran are potent trypsin inhibitors and show therapeutic activity against trypsin-dependent pancreatitis in T7K24R mice. Lack of efficacy in T7D23A mice is probably related to the more severe pathology and insufficient drug concentrations in the pancreas.

Public Data Set of Protein-Ligand Dissociation Kinetic Constants for Quantitative Structure-Kinetics Relationship Studies

Protein-ligand binding affinity reflects the equilibrium thermodynamics of the protein-ligand binding process. Binding/unbinding kinetics is the other side of the coin. Computational models for interpreting the quantitative structure-kinetics relationship (QSKR) aim at predicting protein-ligand binding/unbinding kinetics based on protein structure, ligand structure, or their complex structure, which in principle can provide a more rational basis for structure-based drug design. Thus far, most of the public data sets used for deriving such QSKR models are rather limited in sample size and structural diversity. To tackle this problem, we have compiled a set of 680 protein-ligand complexes with experimental dissociation rate constants (k off), which were mainly curated from the references accumulated for updating our PDBbind database. Three-dimensional structure of each protein-ligand complex in this data set was either retrieved from the Protein Data Bank or carefully modeled based on a proper template. The entire data set covers 155 types of protein, with their dissociation kinetic constants (k off) spanning nearly 10 orders of magnitude. To the best of our knowledge, this data set is the largest of its kind reported publicly. Utilizing this data set, we derived a random forest (RF) model based on protein-ligand atom pair descriptors for predicting k off values. We also demonstrated that utilizing modeled structures as additional training samples will benefit the model performance. The RF model with mixed structures can serve as a baseline for testifying other more sophisticated QSKR models. The whole data set, namely, PDBbind-koff-2020, is available for free download at our PDBbind-CN web site (http://www.pdbbind.org.cn/download.php).

Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking

We present a combined computational approach to protein-ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.

Assessing the binding properties of CASP14 targets and models

An important question is how well the models submitted to CASP retain the properties of target structures. We investigate several properties related to binding. First we explore the binding of small molecules as probes, and count the number of interactions between each residue and such probes, resulting in a binding fingerprint. The similarity between two fingerprints, one for the X-ray structure and the other for a model, is determined by calculating their correlation coefficient. The fingerprint similarity weakly correlates with global measures of accuracy, and GDT_TS higher than 80 is a necessary but not sufficient condition for the conservation of surface binding properties. The advantage of this approach is that it can be carried out without information on potential ligands and their binding sites. The latter information was available for a few targets, and we explored whether the CASP14 models can be used to predict binding sites and to dock small ligands. Finally, we tested the ability of models to reproduce protein-protein interactions by docking both the X-ray structures and the models to their interaction partners in complexes. The analysis showed that in CASP14 the quality of individual domain models is approaching that offered by X-ray crystallography, and hence such models can be successfully used for the identification of binding and regulatory sites, as well as for assembling obligatory protein-protein complexes. Success of ligand docking, however, often depends on fine details of the binding interface, and thus may require accounting for conformational changes by simulation methods.

Conservation of binding properties in protein models

We study the models submitted to round 12 of the Critical Assessment of protein Structure Prediction (CASP) experiment to assess how well the binding properties are conserved when the X-ray structures of the target proteins are replaced by their models. To explore small molecule binding we generate distributions of molecular probes - which are fragment-sized organic molecules of varying size, shape, and polarity - around the protein, and count the number of interactions between each residue and the probes, resulting in a vector of interactions we call a binding fingerprint. The similarity between two fingerprints, one for the X-ray structure and the other for a model of the protein, is determined by calculating the correlation coefficient between the two vectors. The resulting correlation coefficients are shown to correlate with global measures of accuracy established in CASP, and the relationship yields an accuracy threshold that has to be reached for meaningful binding surface conservation. The clusters formed by the probe molecules reliably predict binding hot spots and ligand binding sites in both X-ray structures and reasonably accurate models of the target, but ensembles of models may be needed for assessing the availability of proper binding pockets. We explored ligand docking to the few targets that had bound ligands in the X-ray structure. More targets were available to assess the ability of the models to reproduce protein-protein interactions by docking both the X-ray structures and models to their interaction partners in complexes. It was shown that this application is more difficult than finding small ligand binding sites, and the success rates heavily depend on the local structure in the potential interface. In particular, predicted conformations of flexible loops are frequently incorrect in otherwise highly accurate models, and may prevent predicting correct protein-protein interactions.

Structural analyses of PCNA from the fungal pathogen Candida albicans identify three regions with species-specific conformations

An assembly of multiprotein complexes achieves chromosomal DNA replication at the replication fork. In eukaryotes, proliferating cell nuclear antigen (PCNA) plays a vital role in the assembly of multiprotein complexes at the replication fork and is essential for cell viability. PCNA from several organisms, including Saccharomyces cerevisiae, has been structurally characterised. However, the structural analyses of PCNA from fungal pathogens are limited. Recently, we have reported that PCNA from the opportunistic fungal pathogen Candida albicans complements the essential functions of ScPCNA in S. cerevisiae. Still, it only partially rescues the loss of ScPCNA when the yeast cells are under genotoxic stress. To understand this further, herein, we have determined the crystal structure of CaPCNA and compared that with the existing structures of other fungal and human PCNA. Our comparative structural and in-solution small-angle X-ray scattering (SAXS) analyses reveal that CaPCNA forms a stable homotrimer, both in crystal and in solution. It displays noticeable structural alterations in the oligomerisation interface, P-loop and hydrophobic pocket regions, suggesting its differential function in a heterologous system and avenues for developing specific therapeutics. DATABASES: The PDB and SASBDB accession codes for CaPCNA are 7BUP and SASDHQ9, respectively.

Actionable Cytopathogenic Host Responses of Human Alveolar Type 2 Cells to SARS-CoV-2

Human transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causative pathogen of the COVID-19 pandemic, exerts a massive health and socioeconomic crisis. The virus infects alveolar epithelial type 2 cells (AT2s), leading to lung injury and impaired gas exchange, but the mechanisms driving infection and pathology are unclear. We performed a quantitative phosphoproteomic survey of induced pluripotent stem cell-derived AT2s (iAT2s) infected with SARS-CoV-2 at air-liquid interface (ALI). Time course analysis revealed rapid remodeling of diverse host systems, including signaling, RNA processing, translation, metabolism, nuclear integrity, protein trafficking, and cytoskeletal-microtubule organization, leading to cell cycle arrest, genotoxic stress, and innate immunity. Comparison to analogous data from transformed cell lines revealed respiratory-specific processes hijacked by SARS-CoV-2, highlighting potential novel therapeutic avenues that were validated by a high hit rate in a targeted small molecule screen in our iAT2 ALI system.

Recent Advances in Molecular Docking for the Research and Discovery of Potential Marine Drugs

Marine drugs have long been used and exhibit unique advantages in clinical practices. Among the marine drugs that have been approved by the Food and Drug Administration (FDA), the protein–ligand interactions, such as cytarabine–DNA polymerase, vidarabine–adenylyl cyclase, and eribulin–tubulin complexes, are the important mechanisms of action for their efficacy. However, the complex and multi-targeted components in marine medicinal resources, their bio-active chemical basis, and mechanisms of action have posed huge challenges in the discovery and development of marine drugs so far, which need to be systematically investigated in-depth. Molecular docking could effectively predict the binding mode and binding energy of the protein–ligand complexes and has become a major method of computer-aided drug design (CADD), hence this powerful tool has been widely used in many aspects of the research on marine drugs. This review introduces the basic principles and software of the molecular docking and further summarizes the applications of this method in marine drug discovery and design, including the early virtual screening in the drug discovery stage, drug target discovery, potential mechanisms of action, and the prediction of drug metabolism. In addition, this review would also discuss and prospect the problems of molecular docking, in order to provide more theoretical basis for clinical practices and new marine drug research and development.