Parallel multi-swarm cooperative particle swarm optimization for protein–ligand docking and virtual screening

BACKGROUND: A high-quality docking method tends to yield multifold gains with half pains for the new drug development. Over the past few decades, great efforts have been made for the development of novel docking programs with great efficiency and intriguing accuracy. AutoDock Vina (Vina) is one of these achievements with improved speed and accuracy compared to AutoDock4. Since it was proposed, some of its variants, such as PSOVina and GWOVina, have also been developed. However, for all these docking programs, there is still large room for performance improvement. RESULTS: In this work, we propose a parallel multi-swarm cooperative particle swarm model, in which one master swarm and several slave swarms mutually cooperate and co-evolve. Our experiments show that multi-swarm programs possess better docking robustness than PSOVina. Moreover, the multi-swarm program based on random drift PSO can achieve the best highest accuracy of protein–ligand docking, an outstanding enrichment effect for drug-like activate compounds, and the second best AUC screening accuracy among all the compared docking programs, but with less computation consumption than most of the other docking programs. CONCLUSION: The proposed multi-swarm cooperative model is a novel algorithmic modeling suitable for protein–ligand docking and virtual screening. Owing to the existing coevolution between the master and the slave swarms, this model in parallel generates remarkable docking performance. The source code can be freely downloaded from https://github.com/li-jin-xing/MPSOVina. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04711-0

Comparison of ATP-binding pockets and discovery of homologous recombination inhibitors

The ATP binding sites of many enzymes are structurally related, which complicates their development as therapeutic targets. In this work, we explore a diverse set of ATPases and compare their ATP binding pockets using different strategies, including direct and indirect structural methods, in search of pockets attractive for drug discovery. We pursue different direct and indirect structural strategies, as well as ligandability assessments to help guide target selection. The analyses indicate human RAD51, an enzyme crucial in homologous recombination, as a promising, tractable target. Inhibition of RAD51 has shown promise in the treatment of certain cancers but more potent inhibitors are needed. Thus, we design compounds computationally against the ATP binding pocket of RAD51 with consideration of multiple criteria, including predicted specificity, drug-likeness, and toxicity. The molecules designed are evaluated experimentally using molecular and cell-based assays. Our results provide two novel hit compounds against RAD51 and illustrate a computational pipeline to design new inhibitors against ATPases.This work was supported by Academia Sinica (P.C.), National Taiwan University (P.C.), Taiwan Ministry of Science and Technology (MOST 110-2326-B-002-012 to P.C), Scottish Funding Council (V.B.), and the Generalitat Valenciana and European Social Fund (APOSTD/2020/120 to V.B.).Peer reviewe

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Evidence that nuclear receptors are related to terpene synthases

Ligand-activated nuclear receptors (NRs) orchestrate development, growth, and reproduction across all animal lifeforms – the Metazoa – but how NRs evolved remains mysterious. Given the NR ligands including steroids and retinoids are predominantly terpenoids, we asked whether NRs might have evolved from enzymes that catalyze terpene synthesis and metabolism. We provide evidence suggesting that NRs may be related to the terpene synthase (TS) enzyme superfamily. Based on over 10,000 3D structural comparisons, we report that the NR ligand-binding domain and TS enzymes share a conserved core of seven α-helical segments. In addition, the 3D locations of the major ligand-contacting residues are also conserved between the two protein classes. Primary sequence comparisons reveal suggestive similarities specifically between NRs and the subfamily of cis-isoprene transferases, notably with dehydrodolichyl pyrophosphate synthase and its obligate partner, NUS1/NOGOB receptor. Pharmacological overlaps between NRs and TS enzymes add weight to the contention that they share a distant evolutionary origin, and the combined data raise the possibility that a ligand-gated receptor may have arisen from an enzyme antecedent. However, our findings do not formally exclude other interpretations such as convergent evolution, and further analysis will be necessary to confirm the inferred relationship between the two protein classes

Investigation of cytotoxic properties of some heterocyclic derivatives by molecular modeling approaches

Currently, many technologies have been adopted to boost the efficiency of drug development and overcome obstacles in the drug discovery pipeline. The application of these approaches spans a wide range, from bioactivity predictions, de novo compound synthesis, target identification to hit discovery, and lead optimization. This dissertation comprises two studies. First, we proposed an original approach based on statistical consideration dedicated to k-means clustering analysis in order to define a set of rules for structural features that would help in designing novel anti-cancer drug candidates. It has been applied successfully to classify 500 cytotoxic compounds with 21 molecular descriptors into distinct clusters. The percentage of molecules in each cluster is 50%, 24.88%, and 25.12% for cluster 1, cluster 2, and cluster 3, respectively. Each cluster groups a homogeneous class of molecules with respect to their molecular descriptors. Silhouette analysis, used as a cluster validation approch proves that the molecules are very well clustered, and there are no molecules placed in the wrong cluster. In silico screening of pharmacological properties ADME and evaluation of drug-likeness were performed for all molecules. The quantitative analysis of molecular electrostatic potential was performed to identify the nucleophilic and electrophilic sites in the representative molecule of each cluster. In addition, a molecular docking study was carried out to investigate the interactions of the paragon molecules with the active binding sites of six different targets. Our findings provide a guide to assist the chemist in selecting and testing only the potential molecules with good pharmacokinetic profiles to improve the clinical outcomes of drug therapies. Second, a simulation-based investigation was conducted to examine the CHK1 inhibitory activity of cytotoxic xanthone derivatives using a hierarchical workflow for molecular docking, MD simulation, ADME-TOX prediction, and MEP analysis. A molecular docking study was conducted for the forty-three xanthone derivatives along with standard Prexasertib into the selected CHK1 protein structures 7AKM and 7AKO. Furthermore, MD studies support molecular docking results and validate the stability of studied complexes in physiological conditions. Moreover, in silico ADME-TOX studies are used to predict the pharmacokinetic, pharmacodynamic, and toxicological properties of the selected eight xanthones and the standard Prexasertib. The quantitative analysis of electrostatic potential was performed for the lead compound L36 to identify the reactive sites and possible non- covalent interactions. Our study provides new unexplored insights into xanthones as CHK1 inhibitors and identified L36 as a potential drug candidate that could undergo further in vivo assays and optimization, laying a solid foundation for the development of CHK1 inhibitors and cancer drug discovery. To the best of our knowledge, this is the first time such a study was conducted for the xanthones with CHK1 by using a computational based approach

Investigation of cytotoxic properties of some heterocyclic derivatives by molecular modeling

Currently, many technologies have been adopted to boost the efficiency of drugdevelopment and overcome obstacles in the drug discovery pipeline. The application of these approaches spans a wide range, from bioactivity predictions, de novo compound synthesis, target identification to hit discovery, and lead optimization. This dissertation comprises two studies. First, we proposed an original approach based on statistical consideration dedicated to k-means clustering analysis in order to define a set of rules for structural features that would help in designing novel anti-cancer drug candidates. It has been applied successfully to classify 500 cytotoxic compounds with 21 molecular descriptors into distinct clusters. The percentage of molecules in each cluster is 50%, 24.88%, and 25.12% for cluster 1, cluster 2, and cluster 3, respectively. Each cluster groups a homogeneous class of molecules with respect to their molecular descriptors. Silhouette analysis, used as a cluster validation approach proves that the molecules are very well clustered, and there are no molecules placed in the wrong cluster. In silico screening of pharmacological properties ADME and evaluation of drug-likeness were performed for all molecules. The quantitative analysis of molecular electrostatic potential was performed to identify the nucleophilic and electrophilic sites in the representative molecule of each cluster. In addition, a molecular docking study was carried out to investigate the interactions of the paragon molecules with the active binding sites of six different targets. Our findings provide a guide to assist the chemist in selecting and testing only the potential molecules with good pharmacokinetic profiles to improve the clinical outcomes of drug therapies. Second, a simulation-based investigation was conducted to examine the CHK1 inhibitory activity of cytotoxic xanthone derivatives using a hierarchical workflow for molecular docking, MD simulation, ADME-TOX prediction, and MEP analysis. A molecular docking study was conducted for the forty-three xanthone derivatives along with standard Prexasertib into the selected CHK1 protein structures 7AKM and 7AKO. Furthermore, MD studies support molecular docking results and validate the stability of studied complexes in physiological conditions. Moreover, in silico ADME-TOX studies are used to predict the pharmacokinetic, pharmacodynamic, and toxicological properties of the selected eight xanthones and the standard Prexasertib. The quantitative analysis of electrostatic potential was performed for the lead compound L36 to identify the reactive sites and possible noncovalent interactions. Our study provides new unexplored insights into xanthones as CHK1 inhibitors and identified L36 as a potential drug candidate that could undergo further in vivo assays and optimization, laying a solid foundation for the development of CHK1 inhibitors and cancer drug discovery. To the best of our knowledge, this is the first time such a study was conducted for the xanthones with CHK1 by using a computational based approach

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u