Protein-protein docking dealing with the unknown Moreira, Irina S; Fernandes, Pedro A; Ramos, Maria J
Journal of computational chemistry,
30 January 2010, Letnik:
31, Številka:
2
Journal Article
Recenzirano
Protein-protein binding is one of the critical events in biology, and knowledge of proteic complexes three-dimensional structures is of fundamental importance for the biochemical study of ...pharmacologic compounds. In the past two decades there was an emergence of a large variety of algorithms designed to predict the structures of protein-protein complexes--a procedure named docking. Computational methods, if accurate and reliable, could play an important role, both to infer functional properties and to guide new experiments. Despite the outstanding progress of the methodologies developed in this area, a few problems still prevent protein-protein docking to be a widespread practice in the structural study of proteins. In this review we focus our attention on the principles that govern docking, namely the algorithms used for searching and scoring, which are usually referred as the docking problem. We also focus our attention on the use of a flexible description of the proteins under study and the use of biological information as the localization of the hot spots, the important residues for protein-protein binding. The most common docking softwares are described too.
The pollution of water resources by pesticides poses serious problems for public health and the environment. In this study,
Actinobacteria
strains were isolated from three wastewater treatment plants ...(WWTPs) and were screened for their ability to degrade 17 pesticide compounds. Preliminary screening of 13 of the isolates of
Actinobacteria
allowed the selection of 12 strains with potential for the degradation of nine different pesticides as sole carbon source, including aliette, for which there are no previous reports of biodegradation. Evaluation of the bacterial growth and degradation kinetics of the pesticides 2,4-dichlorophenol (2,4-DCP) and thiamethoxam (tiam) by selected
Actinobacteria
strains was performed in liquid media. Strains
Streptomyces
sp. ML and
Streptomyces
sp. OV were able to degrade 45% of 2,4-DCP (50 mg/l) as the sole carbon source in 30 days and 84% of thiamethoxam (35 mg/l) in the presence of 10 mM of glucose in 18 days. The biodegradation of thiamethoxam by
Actinobacteria
strains was reported for the first time in this study. These strains are promising for use in bioremediation of ecosystems polluted by this type of pesticides.
Cell-penetrating peptides (CPPs) are short chains of amino acids that have shown remarkable potential to cross the cell membrane and deliver coupled therapeutic cargoes into cells. Designing and ...testing different CPPs to target specific cells or tissues is crucial to ensure high delivery efficiency and reduced toxicity. However, in vivo
/
in vitro testing of various CPPs can be both time-consuming and costly, which has led to interest in computational methodologies, such as Machine Learning (ML) approaches, as faster and cheaper methods for CPP design and uptake prediction. However, most ML models developed to date focus on classification rather than regression techniques, because of the lack of informative quantitative uptake values. To address these challenges, we developed POSEIDON, an open-access and up-to-date curated database that provides experimental quantitative uptake values for over 2,300 entries and physicochemical properties of 1,315 peptides. POSEIDON also offers physicochemical properties, such as cell line, cargo, and sequence, among others. By leveraging this database along with cell line genomic features, we processed a dataset of over 1,200 entries to develop an ML regression CPP uptake predictor. Our results demonstrated that POSEIDON accurately predicted peptide cell line uptake, achieving a Pearson correlation of 0.87, Spearman correlation of 0.88, and r
2
score of 0.76, on an independent test set. With its comprehensive and novel dataset, along with its potent predictive capabilities, the POSEIDON database and its associated ML predictor signify a significant leap forward in CPP research and development. The POSEIDON database and ML Predictor are available for free and with a user-friendly interface at
https://moreiralab.com/resources/poseidon/
, making them valuable resources for advancing research on CPP-related topics. Scientific Contribution Statement: Our research addresses the critical need for more efficient and cost-effective methodologies in Cell-Penetrating Peptide (CPP) research. We introduced POSEIDON, a comprehensive and freely accessible database that delivers quantitative uptake values for over 2,300 entries, along with detailed physicochemical profiles for 1,315 peptides. Recognizing the limitations of current Machine Learning (ML) models for CPP design, our work leveraged the rich dataset provided by POSEIDON to develop a highly accurate ML regression model for predicting CPP uptake.
Graphical Abstract
We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the ...structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall’s Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening.
The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected ...toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein–ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.
Abstract
Background
In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data ...encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)–powered informational view that integrates the relevant scientific fields and explores new territories.
Results
Here, we show SYNPRED, an interdisciplinary approach that leverages specifically designed ensembles of AI algorithms, as well as links omics and biophysical traits to predict anticancer drug synergy. It uses 5 reference models (Bliss, Highest Single Agent, Loewe, Zero Interaction Potency, and Combination Sensitivity Score), which, coupled with AI algorithms, allowed us to attain the ones with the best predictive performance and pinpoint the most appropriate reference model for synergy prediction, often overlooked in similar studies. By using an independent test set, SYNPRED exhibits state-of-the-art performance metrics either in the classification (accuracy, 0.85; precision, 0.91; recall, 0.90; area under the receiver operating characteristic, 0.80; and F1-score, 0.91) or in the regression models, mainly when using the Combination Sensitivity Score synergy reference model (root mean square error, 11.07; mean squared error, 122.61; Pearson, 0.86; mean absolute error, 7.43; Spearman, 0.87). Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application was constructed, allowing easy access by nonexpert researchers.
Conclusions
The performance of SYNPRED rivals that of the existing methods that tackle the same problem, yielding unbiased results trained with one of the most comprehensive datasets available (NCI ALMANAC). The leveraging of different reference models allowed deeper insights into which of them can be more appropriately used for synergy prediction. The Combination Sensitivity Score clearly stood out with improved performance among the full scope of surveyed approaches and synergy reference models. Furthermore, SYNPRED takes a particular focus on data interpretability, which has been in the spotlight lately when using the most advanced AI techniques.
Understanding protein–protein association and being able to determine the crucial residues responsible for their association (hot-spots) is a key issue with huge practical applications such as ...rational drug design and protein engineering. A variety of computational methods exist to detect hot-spots residues, but the development of a fast and accurate quantitative alanine scanning mutagenesis (ASM) continues to be crucial. Using four protein–protein complexes, we have compared a variation of the standard computational ASM protocol developed at our group, based on the Molecular Mechanics/Poisson–Boltzmann Surface Area (MM-PBSA) approach, against Thermodynamic Integration (TI), a well-known and accurate but computationally expensive method. To compare the efficiency and the accuracy of the two methods, we have calculated the protein–protein binding free energy differences upon alanine mutation of interfacial residues (ΔΔG bind). In relation to the experimental ΔΔG bind values, the average error obtained with TI was 1.53 kcal/mol, while the ASM protocol resulted in an average error of 1.18 kcal/mol. The results demonstrate that the much faster ASM protocol gives results at the same level of accuracy as the TI method but at a fraction of the computational time required to run TI. This ASM protocol is therefore a strong and efficient alternative to the systematic evaluation of protein–protein interfaces, involving hundreds of amino acid residues in search of hot-spots.
The General AMBER Force Field (GAFF) has been extended to describe a series of selenium and tellurium diphenyl dichalcogenides. These compounds, besides being eco-friendly catalysts for numerous ...oxidations in organic chemistry, display peroxidase activity, i.e., can reduce hydrogen peroxide and harmful organic hydroperoxides to water/alcohols and as such are very promising antioxidant drugs. The novel GAFF parameters are tested in MD simulations in different solvents and the 77Se NMR chemical shift of diphenyl diselenide is computed using structures extracted from MD snapshots and found in nice agreement with the measured value in CDCl3. The whole computational protocol is described in detail and integrated with in-house code to allow easy derivation of the force field parameters for analogous compounds as well as for Se/Te organocompounds in general.
The occurrence of endocrine disrupting chemicals (EDCs) is a major issue for marine and coastal environments in the proximity of urban areas. The occurrence of EDCs in the Pearl River Delta region is ...well documented but specific data related to Macao is unavailable. The levels of bisphenol-A (BPA), estrone (E1), 17α-estradiol (αE2), 17β-estradiol (E2), estriol (E3), and 17α-ethynylestradiol (EE2) were measured in sediment samples collected along the coastline of Macao. BPA was found in all 45 collected samples with lower BPA concentrations associated to the presence of mangrove trees. Biodegradation assays were performed to evaluate the capacity of the microbial communities of the surveyed ecosystems to degrade BPA and its analogue BPS. Using sediments collected at a WWTP discharge point as inoculum, at a concentration of 2 mg l
−1
complete removal of BPA was observed within 6 days, whereas for the same concentration BPS removal was of 95% after 10 days, which is particularly interesting since this compound is considered recalcitrant to biodegradation and likely to accumulate in the environment. Supplementation with BPA improved the degradation of bisphenol-S (BPS). Aiming at the isolation of EDCs-degrading bacteria, enrichments were established with sediments supplied with BPA, BPS, E2 and EE2, which led to the isolation of a bacterial strain, identified as
Rhodoccoccus
sp. ED55, able to degrade the four compounds at different extents. The isolated strain represents a valuable candidate for bioremediation of contaminated soils and waters.
Proteins and protein-based complexes are the basis of many key systems in nature and have been the subject of intense research in the last decades, in an attempt to acquire comprehensive knowledge of ...reactions that take place in nature. Computational Alanine Scanning Mutagenesis approaches have been extensively used in the study of protein interfaces and in the determination of the most important residues for complex formation, the Hot-spots. However, as it is usually applied to the study of protein–protein interfaces, we tried to modify and apply it to the study of protein–DNA interfaces, which are also crucial in nature but have not been the subject of as much research. In this work, we carry out MD simulations of seven protein–DNA complexes and tested the influence of the variation of different parameters on the determination of the binding free energy terms (ΔΔG binding) of 78 mutations: solvent representation, internal dielectric constant, Linear and Nonlinear Poisson–Boltzmann equation, Generalized Born model, simulation time, number of structures analyzed, number of MD trajectories, force field used, and energetic terms involved. Overall, this new approach gave an average error of 1.55 kcal/mol, and P, R, F1, accuracy, and specificity values of 0.78, 0.50, 0.61, 0.77, and 0.92, respectively. This improved computational alanine scanning mutagenesis approach may serve as a tool to explore the behavior of this important class of complexes.
PDF
