scholarly journals Leveraging non-structural data to predict structures of protein–ligand complexes

2020 ◽  
Author(s):  
Joseph M. Paggi ◽  
Julia A. Belk ◽  
Scott A. Hollingsworth ◽  
Nicolas Villanueva ◽  
Alexander S. Powers ◽  
...  
Keyword(s):  
Drug Targets ◽  
3D Structure ◽  
Three Dimensional ◽  
Structural Data ◽  
Rational Drug Design ◽  
Pose Prediction ◽  
Sources Of Information ◽  
Statistical Framework ◽  
Rational Drug ◽  
Ligand Interactions

AbstractOver the past fifty years, tremendous effort has been devoted to computational methods for predicting properties of ligands that bind macromolecular targets, a problem critical to rational drug design. Such methods generally fall into two categories: physics-based methods, which directly model ligand interactions with the target given the target’s three-dimensional (3D) structure, and ligand-based methods, which predict ligand properties given experimental measurements for similar ligands. Here we present a rigorous statistical framework to combine these two sources of information. We develop a method to predict a ligand’s pose—the 3D structure of the ligand bound to its protein target—that leverages a widely available source of information: a list of other ligands that are known to bind the same target but for which no 3D structure is available. This combination of physics-based and ligand-based modeling improves upon state-of-the-art pose prediction accuracy across all major families of drug targets. As an illustrative application, we predict binding poses of antipsychotics and validate the results experimentally. Our statistical framework and results suggest broad opportunities to predict diverse ligand properties using machine learning methods that draw on physical modeling and ligand data simultaneously.

scholarly journals Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes

2021 ◽  
Vol 118 (51) ◽  
pp. e2112621118
Author(s):  
Joseph M. Paggi ◽  
Julia A. Belk ◽  
Scott A. Hollingsworth ◽  
Nicolas Villanueva ◽  
Alexander S. Powers ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Drug Targets ◽  
3D Structure ◽  
Three Dimensional ◽  
Rational Drug Design ◽  
Screening Methods ◽  
Pose Prediction ◽  
Learning Approaches ◽  
Sources Of Information ◽  
Drug Candidates

Over the past five decades, tremendous effort has been devoted to computational methods for predicting properties of ligands—i.e., molecules that bind macromolecular targets. Such methods, which are critical to rational drug design, fall into two categories: physics-based methods, which directly model ligand interactions with the target given the target’s three-dimensional (3D) structure, and ligand-based methods, which predict ligand properties given experimental measurements for similar ligands. Here, we present a rigorous statistical framework to combine these two sources of information. We develop a method to predict a ligand’s pose—the 3D structure of the ligand bound to its target—that leverages a widely available source of information: a list of other ligands that are known to bind the same target but for which no 3D structure is available. This combination of physics-based and ligand-based modeling improves pose prediction accuracy across all major families of drug targets. Using the same framework, we develop a method for virtual screening of drug candidates, which outperforms standard physics-based and ligand-based virtual screening methods. Our results suggest broad opportunities to improve prediction of various ligand properties by combining diverse sources of information through customized machine-learning approaches.

scholarly journals Crystallizing the Parkinson’s Disease Protein LRRK2 Under Microgravity Conditions

2018 ◽  
Author(s):  
Sebastian Mathea ◽  
Marco Baptista ◽  
Paul Reichert ◽  
April Spinale ◽  
Jian Wu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
3D Structure ◽  
Space Station ◽  
Rational Drug Design ◽  
Crystal Formation ◽  
Gene Coding ◽  
Rational Drug ◽  
Disease Protein ◽  
Microgravity Conditions

AbstractMutations in the gene coding for leucine-rich repeat kinase 2 (LRRK2) are a considerable cause for Parkinson’s disease (PD). However, the high- resolution 3D structure of the protein is still lacking. This structure will not only help to understand PD etiology but will also enable rational drug design. We have established a reliable method to produce LRRK2 crystals for the first time. However, the limited resolution of the diffraction data prevented structure determination using crystallographic methods. Herein we describe our efforts to improve the crystal quality by crystallizing under microgravity conditions aboard the International Space Station (ISS). Our method features diffusive sample mixing in capillaries and controlled crystal formation by transporting the samples in a frozen state. The crystallisation was successfully repeated under microgravity conditions. However, comparison of earth-grown and microgravity-grown LRRK2 crystals did not reveal any differences in diffraction quality. Here we present the established protocol and our experience adapting crystallization condition to the requirements necessary for successful crystallization of large and sensitive biomolecules under microgravity.

scholarly journals NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Homa MohammadiPeyhani ◽  
Anush Chiappino-Pepe ◽  
Kiandokht Haddadi ◽  
Jasmin Hafner ◽  
Noushin Hadadi ◽  
...  
Keyword(s):  
Drug Targets ◽  
Drug Repurposing ◽  
Rational Drug Design ◽  
Bioactive Molecules ◽  
Lead Discovery ◽  
Minimal Impact ◽  
Practical Applications ◽  
Rational Drug ◽  
Golden Standard ◽  
And Performance

The discovery of a drug requires over a decade of intensive research and financial investments – and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug–drug and drug–metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

Structural Analysis of Proteins on Lipid Substrates

2000 ◽  
Vol 6 (S2) ◽  
pp. 1182-1183
Author(s):  
Elizabeth M. Wilson-Kubalek
Keyword(s):  
Structural Information ◽  
Rapid Determination ◽  
3D Structure ◽  
Three Dimensional ◽  
Molecular Complexes ◽  
Structural Data ◽  
X Ray Crystallography ◽  
Helical Protein ◽  
3D Structure Determination

Electron microscopy (EM) has become an increasingly powerful method for the determination of three-dimensional (3D) structures of proteins and macromolecular complexes. EM offers advantages over X-ray crystallography and NMR for obtaining structural information about proteins in physiological conditions, as components of large assemblies, that cannot be obtained in large quantity, or that fail to yield 3D crystals. EM has been used to obtain structural data from images of isolated molecules and molecular complexes, two-dimensional (2D) protein crystals, and helical protein arrays. Helically arranged proteins allow the most rapid determination of 3D maps because they contain a complete range of equally spaced molecular views, therefore no tilting of the sample with respect to the electron beam is required. However, so far 3D structure determination of helical assemblies has been limited to proteins that naturally adopt this organization and to proteins that fortuitously crystallize as helices.

Computational and Pharmacogenomic Insights on Hypertension Treatment: Rational Drug Design and Optimization Strategies

2019 ◽  
Vol 21 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Lakshmanan Loganathan ◽  
Krishnasamy Gopinath ◽  
Vadivel Murugan Sankaranarayanan ◽  
Ritushree Kukreti ◽  
Kannan Rajendran ◽  
...  
Keyword(s):  
Drug Design ◽  
Drug Targets ◽  
Antihypertensive Drugs ◽  
Rational Drug Design ◽  
Hypertension Treatment ◽  
Genetic Traits ◽  
Drug Responses ◽  
Computational Screening ◽  
Rational Drug ◽  
Multiple Drugs

Background:: Hypertension is a prevalent cardiovascular complication caused by genetic and nongenetic factors. Blood pressure (BP) management is difficult because most patients become resistant to monotherapy soon after treatment initiation. Although many antihypertensive drugs are available, some patients do not respond to multiple drugs. Identification of personalized antihypertensive treatments is a key for better BP management. Objective:: This review aimed to elucidate aspects of rational drug design and other methods to develop better hypertension management. Results:: Among hypertension-related signaling mechanisms, the renin-angiotensin-aldosterone system is the leading genetic target for hypertension treatment. Identifying a single drug that acts on multiple targets is an emerging strategy for hypertension treatment, and could be achieved by discovering new drug targets with less mutated and highly conserved regions. Extending pharmacogenomics research to include patients with hypertension receiving multiple antihypertensive drugs could help identify the genetic markers of hypertension. However, available evidence on the role of pharmacogenomics in hypertension is limited and primarily focused on candidate genes. Studies on hypertension pharmacogenomics aim to identify the genetic causes of response variations to antihypertensive drugs. Genetic association studies have identified single nucleotide polymorphisms affecting drug responses. To understand how genetic traits alter drug responses, computational screening of mutagenesis can be utilized to observe drug response variations at the protein level, which can help identify new inhibitors and drug targets to manage hypertension. Conclusions:: Rational drug design facilitates the discovery and design of potent inhibitors. However, further research and clinical validation are required before novel inhibitors can be clinically used as antihypertensive therapies.

First model of dimeric LRRK2: the challenge of unrevealing the structure of a multidomain Parkinson's-associated protein

2016 ◽  
Vol 44 (6) ◽  
pp. 1635-1641 ◽  
Author(s):  
Giambattista Guaitoli ◽  
Bernd K. Gilsbach ◽  
Francesco Raimondi ◽  
Christian Johannes Gloeckner
Keyword(s):  
Kinase Activity ◽  
Structural Model ◽  
Three Dimensional ◽  
Kinase Domain ◽  
Rational Drug Design ◽  
Dimensional Structure ◽  
Rational Drug ◽  
Its Gene ◽  
Lrrk2 Gene ◽  
Successful Approach

Mutations within the leucine-rich repeat kinase 2 (LRRK2) gene represent the most common cause of Mendelian forms of Parkinson's disease, among autosomal dominant cases. Its gene product, LRRK2, is a large multidomain protein that belongs to the Roco protein family exhibiting GTPase and kinase activity, with the latter activity increased by pathogenic mutations. To allow rational drug design against LRRK2 and to understand the cross-regulation of the G- and the kinase domain at a molecular level, it is key to solve the three-dimensional structure of the protein. We review here our recent successful approach to build the first structural model of dimeric LRRK2 by an integrative modeling approach.

scholarly journals Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

2019 ◽  
Vol 26 (21) ◽  
pp. 3874-3889 ◽  
Author(s):  
Jelica Vucicevic ◽  
Katarina Nikolic ◽  
John B.O. Mitchell
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Antineoplastic Agents ◽  
Drug Targets ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Rational Drug Design ◽  
Bioactive Molecules ◽  
Computational Approaches ◽  
Rational Drug

Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile. Conclusion: In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

scholarly journals PDB_REDO: automated re-refinement of X-ray structure models in the PDB

2009 ◽  
Vol 42 (3) ◽  
pp. 376-384 ◽  
Author(s):  
Robbie P. Joosten ◽  
Jean Salzemann ◽  
Vincent Bloch ◽  
Heinz Stockinger ◽  
Ann-Charlott Berglund ◽  
...  
Keyword(s):  
Structural Biology ◽  
Large Scale ◽  
Homology Modelling ◽  
Three Dimensional ◽  
Data Bank ◽  
Rational Drug Design ◽  
X Ray ◽  
Rational Drug ◽  
Geometric Quality ◽  
Existing Structure

Structural biology, homology modelling and rational drug design require accurate three-dimensional macromolecular coordinates. However, the coordinates in the Protein Data Bank (PDB) have not all been obtained using the latest experimental and computational methods. In this study a method is presented for automated re-refinement of existing structure models in the PDB. A large-scale benchmark with 16 807 PDB entries showed that they can be improved in terms of fit to the deposited experimental X-ray data as well as in terms of geometric quality. The re-refinement protocol uses TLS models to describe concerted atom movement. The resulting structure models are made available through the PDB_REDO databank (http://www.cmbi.ru.nl/pdb_redo/). Grid computing techniques were used to overcome the computational requirements of this endeavour.

Computational Modeling of Solvent Effects on Protein-Ligand Interactions Using Fully Polarizable Continuum Model and Rational Drug Design

2013 ◽  
Vol 13 (1) ◽  
pp. 31-60 ◽  
Author(s):  
Fang Zheng ◽  
Chang-Guo Zhan
Keyword(s):  
Drug Design ◽  
Computational Modeling ◽  
Molecular Species ◽  
Solvent Effects ◽  
Polarizable Continuum Model ◽  
Rational Drug Design ◽  
Free Energies ◽  
Protein Ligand Interactions ◽  
Rational Drug ◽  
Ligand Interactions

AbstractThis is a brief review of the computational modeling of protein-ligand interactions using a recently developed fully polarizable continuum model (FPCM) and rational drug design. Computational modeling has become a powerful tool in understanding detailed protein-ligand interactions at molecular level and in rational drug design. To study the binding of a protein with multiple molecular species of a ligand, one must accurately determine both the relative free energies of all of the molecular species in solution and the corresponding microscopic binding free energies for all of the molecular species binding with the protein. In this paper, we aim to provide a brief overview of the recent development in computational modeling of the solvent effects on the detailed protein-ligand interactions involving multiple molecular species of a ligand related to rational drug design. In particular, we first briefly discuss the main challenges in computational modeling of the detailed protein-ligand interactions involving the multiple molecular species and then focus on the FPCM model and its applications. The FPCM method allows accurate determination of the solvent effects in the first-principles quantum mechanism (QM) calculations on molecules in solution. The combined use of the FPCM-based QM calculations and other computational modeling and simulations enables us to accurately account for a protein binding with multiple molecular species of a ligand in solution. Based on the computational modeling of the detailed protein-ligand interactions, possible new drugs may be designed rationally as either small-molecule ligands of the protein or engineered proteins that bind/metabolize the ligand. The computational drug design has successfully led to discovery and development of promising drugs.

scholarly journals AlphaFold2 transmembrane protein structure prediction shines

2021 ◽  
Author(s):  
Tamas Hegedus ◽  
Markus Geisler ◽  
Gergely Lukacs ◽  
Bianka Farkas
Keyword(s):  
Prescription Drugs ◽  
Protein Function ◽  
Structure Prediction ◽  
Drug Targets ◽  
Transmembrane Protein ◽  
Structural Data ◽  
Data Bank ◽  
Rational Drug Design ◽  
Abc Protein ◽  
Dynamics Simulations

Transmembrane (TM) proteins are major drug targets, indicated by the high percentage of prescription drugs acting on them. For a rational drug design and an understanding of mutational effects on protein function, structural data at atomic resolution are required. However, hydrophobic TM proteins often resist experimental structure determination and in spite of the increasing number of cryo-EM structures, the available TM folds are still limited in the Protein Data Bank. Recently, the DeepMind's AlphaFold2 machine learning method greatly expanded the structural coverage of sequences, with high accuracy. Since the employed algorithm did not take specific properties of TM proteins into account, the validity of the generated TM structures should be assessed. Therefore, we investigated the quality of structures at genome scales, at the level of ABC protein superfamily folds, and also in specific individual cases. We tested template-free structure prediction also with a new TM fold, dimer modeling, and stability in molecular dynamics simulations. Our results strongly suggest that AlphaFold2 performs astoundingly well in the case of TM proteins and that its neural network is not overfitted. We conclude that a careful application of its structural models will advance TM protein associated studies at an unexpected level.

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