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Article ; Online: Docking approaches for modeling multi-molecular assemblies.

Rosell, Mireia / Fernández-Recio, Juan

2020 Volume 64, Page(s) 59–65

Abstract: Computational docking approaches aim to overcome the limited availability of experimental structural data on protein-protein interactions, which are key in biology. The field is rapidly moving from the traditional docking methodologies for modeling of ... ...

Abstract	Computational docking approaches aim to overcome the limited availability of experimental structural data on protein-protein interactions, which are key in biology. The field is rapidly moving from the traditional docking methodologies for modeling of binary complexes to more integrative approaches using template-based, data-driven modeling of multi-molecular assemblies. We will review here the predictive capabilities of current docking methods in blind conditions, based on the results from the most recent community-wide blind experiments. Integration of template-based and ab initio docking approaches is emerging as the optimal strategy for modeling protein complexes and multimolecular assemblies. We will also review the new methodological advances on ab initio docking and integrative modeling.
MeSH term(s)	Computational Biology ; Molecular Docking Simulation ; Protein Binding ; Proteins/metabolism ; Software
Chemical Substances	Proteins
Keywords	covid19
Language	English
Publishing date	2020-06-29
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	1068353-7
ISSN	1879-033X ; 0959-440X
ISSN (online)	1879-033X
ISSN	0959-440X
DOI	10.1016/j.sbi.2020.05.016
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Article: Docking-based identification of small-molecule binding sites at protein-protein interfaces.

Rosell, Mireia / Fernández-Recio, Juan

Computational and structural biotechnology journal

2020 Volume 18, Page(s) 3750–3761

Abstract: Protein-protein interactions play an essential role in many biological processes, and their perturbation is a major cause of disease. The use of small molecules to modulate them is attracting increased attention, but protein interfaces generally do not ... ...

Abstract	Protein-protein interactions play an essential role in many biological processes, and their perturbation is a major cause of disease. The use of small molecules to modulate them is attracting increased attention, but protein interfaces generally do not have clear cavities for binding small compounds. A proposed strategy is to target interface hot-spot residues, but their identification through computational approaches usually require the complex structure, which is not often available. In this context, pyDock energy-based docking and scoring can predict hot-spots on the unbound proteins, thus not requiring the complex structure. Here, we have devised a new strategy to detect protein-protein inhibitor binding sites, based on the integration of molecular dynamics for the generation of transient cavities, and docking-based interface hot-spot prediction for the selection of the suitable cavities. This integrative approach has been validated on a test set formed by protein-protein complexes with known inhibitors for which complete structural data of unbound molecules and complexes is available. The results show that local conformational sampling with short molecular dynamics can generate transient cavities similar to the known inhibitor binding sites, and that docking simulations can identify the best cavities with similar predictive accuracy as when knowing the real interface. In a few cases, these predicted pockets are shown to be suitable for protein-ligand docking. The proposed strategy will be useful for many protein-protein complexes for which there is no available structure, as long as the the unbound proteins do not deviate dramatically from the bound conformations.
Language	English
Publishing date	2020-11-21
Publishing country	Netherlands
Document type	Journal Article
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2020.11.029
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Article: Kisspeptins and norepinephrine regulate different G-protein-coupled receptor signaling pathways.

Rosell, Rafael / González-Cao, María / Ito, Masaoki / Jordan, Mireia Marco / Gómez-Vázquez, José Luis / Chen, Jing-Hua / Aguilar, Andrés / Chaib, Imane

Translational lung cancer research

2022 Volume 11, Issue 8, Page(s) 1517–1521

Language	English
Publishing date	2022-09-01
Publishing country	China
Document type	Editorial ; Comment
ZDB-ID	2754335-3
ISSN	2226-4477 ; 2218-6751
ISSN (online)	2226-4477
ISSN	2218-6751
DOI	10.21037/tlcr-22-494
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Article: Docking approaches for modeling multi-molecular assemblies

Rosell, Mireia / Fernández-Recio, Juan

Curr Opin Struct Biol

Abstract	Computational docking approaches aim to overcome the limited availability of experimental structural data on protein-protein interactions, which are key in biology. The field is rapidly moving from the traditional docking methodologies for modeling of binary complexes to more integrative approaches using template-based, data-driven modeling of multi-molecular assemblies. We will review here the predictive capabilities of current docking methods in blind conditions, based on the results from the most recent community-wide blind experiments. Integration of template-based and ab initio docking approaches is emerging as the optimal strategy for modeling protein complexes and multimolecular assemblies. We will also review the new methodological advances on ab initio docking and integrative modeling.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #624039
Database	COVID19

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Article ; Online: Hot-spot analysis for drug discovery targeting protein-protein interactions.

Rosell, Mireia / Fernández-Recio, Juan

Expert opinion on drug discovery

2018 Volume 13, Issue 4, Page(s) 327–338

Abstract: Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly ... ...

Abstract	Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.
MeSH term(s)	Computational Biology/methods ; Drug Design ; Drug Discovery/methods ; Humans ; Models, Molecular ; Molecular Docking Simulation ; Molecular Dynamics Simulation ; Proteins/metabolism
Chemical Substances	Proteins
Language	English
Publishing date	2018-01-29
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2259618-5
ISSN	1746-045X ; 1746-0441
ISSN (online)	1746-045X
ISSN	1746-0441
DOI	10.1080/17460441.2018.1430763
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Article ; Online: Docking-based identification of small-molecule binding sites at protein-protein interfaces

Rosell, Mireia / Fernández-Recio, Juan

Abstract	Protein-protein interactions play an essential role in many biological processes, and their perturbation is a major cause of disease. The use of small molecules to modulate them is attracting increased attention, but protein interfaces generally do not have clear cavities for binding small compounds. A proposed strategy is to target interface hot-spot residues, but their identification through computational approaches usually require the complex structure, which is not often available. In this context, pyDock energy-based docking and scoring can predict hot-spots on the unbound proteins, thus not requiring the complex structure. Here, we have devised a new strategy to detect protein-protein inhibitor binding sites, based on the integration of molecular dynamics for the detection of transient cavities, and docking-based interface hot-spot prediction for the selection of the suitable cavities. This integrative approach has been validated on a test set formed by protein-protein complexes with known inhibitors for which complete structural data of unbound molecules and complexes is available. The results show that local conformational sampling with short molecular dynamics can generate transient cavities similar to the known inhibitor binding sites, and that docking simulations can identify the best cavities with similar predictive accuracy as when knowing the real interface. In a few cases, these predicted pockets are shown to be suitable for protein-ligand docking. The proposed strategy will be useful for many protein-protein complexes for which there is no available structure, as long as the the unbound proteins do not deviate dramatically from the bound conformations.
Keywords	covid19
Publisher	Elsevier
Document type	Article ; Online
DOI	10.1016/j.csbj.2020.11.029
Database	COVID19

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Article ; Online: Modeling of Protein Complexes and Molecular Assemblies with pyDock.

Rosell, Mireia / Rodríguez-Lumbreras, Luis Angel / Fernández-Recio, Juan

Methods in molecular biology (Clifton, N.J.)

2020 Volume 2165, Page(s) 175–198

Abstract: The study of the 3D structural details of protein interactions is essential to understand biomolecular functions at the molecular level. In this context, the limited availability of experimental structures of protein-protein complexes at atomic ... ...

Abstract	The study of the 3D structural details of protein interactions is essential to understand biomolecular functions at the molecular level. In this context, the limited availability of experimental structures of protein-protein complexes at atomic resolution is propelling the development of computational docking methods that aim to complement the current structural coverage of protein interactions. One of these docking approaches is pyDock, which uses van der Waals, electrostatics, and desolvation energy to score docking poses generated by a variety of sampling methods, typically FTDock or ZDOCK. The method has shown a consistently good prediction performance in community-wide assessment experiments like CAPRI or CASP, and has provided biological insights and insightful interpretation of experiments by modeling many biomolecular interactions of biomedical and biotechnological interest. Here, we describe in detail how to perform structural modeling of protein assemblies with pyDock, and the application of its modules to different biomolecular recognition phenomena, such as modeling of binding mode, interface, and hot-spot prediction, use of restraints based on experimental data, inclusion of low-resolution structural data, binding affinity estimation, or modeling of homo- and hetero-oligomeric assemblies.
MeSH term(s)	Binding Sites ; Molecular Docking Simulation/methods ; Protein Binding ; Protein Multimerization ; Software
Language	English
Publishing date	2020-07-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-0708-4_10
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Article ; Online: PirePred: An Accurate Online Consensus Tool to Interpret Newborn Screening-Related Genetic Variants in Structural Context.

Galano-Frutos, Juan José / García-Cebollada, Helena / López, Alfonso / Rosell, Mireia / de la Cruz, Xavier / Fernández-Recio, Juan / Sancho, Javier

The Journal of molecular diagnostics : JMD

2022 Volume 24, Issue 4, Page(s) 406–425

Abstract: PirePred is a genetic interpretation tool used for a variety of medical conditions investigated in newborn screening programs. The PirePred server retrieves, analyzes, and displays in real time genetic and structural data on 58 genes/proteins associated ... ...

Abstract	PirePred is a genetic interpretation tool used for a variety of medical conditions investigated in newborn screening programs. The PirePred server retrieves, analyzes, and displays in real time genetic and structural data on 58 genes/proteins associated with medical conditions frequently investigated in the newborn. PirePred analyzes the predictions generated by 15 pathogenicity predictors and applies an optimized majority vote algorithm to classify any possible nonsynonymous single-nucleotide variant as pathogenic, benign, or of uncertain significance. PirePred predictions for variants of clear clinical significance are better than those of any of the individual predictors considered (based on accuracy, sensitivity, and negative predictive value) or are among the best ones (for positive predictive value and Matthews correlation coefficient). PirePred predictions also outperform the comparable in silico predictions offered as supporting evidence, according to American College of Medical Genetics and Genomics guidelines, by VarSome and Franklin. Also, PirePred has very high prediction coverage. To facilitate the molecular interpretation of the missense, nonsense, and frameshift variants in ClinVar, the changing amino acid residue is displayed in its structural context, which is analyzed to provide functional clues. PirePred is an accurate, robust, and easy-to-use tool for clinicians involved in neonatal screening programs and for researchers of related diseases. The server is freely accessible and provides a user-friendly gateway into the structural/functional consequences of genetic variants at the protein level.
MeSH term(s)	Algorithms ; Consensus ; Genomics ; Humans ; Infant, Newborn ; Mutation, Missense ; Neonatal Screening
Language	English
Publishing date	2022-02-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2000060-1
ISSN	1943-7811 ; 1525-1578
ISSN (online)	1943-7811
ISSN	1525-1578
DOI	10.1016/j.jmoldx.2022.01.005
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Article ; Online: Structural and Computational Characterization of Disease-Related Mutations Involved in Protein-Protein Interfaces.

Navío, Dàmaris / Rosell, Mireia / Aguirre, Josu / de la Cruz, Xavier / Fernández-Recio, Juan

International journal of molecular sciences

2019 Volume 20, Issue 7

Abstract: One of the known potential effects of disease-causing amino acid substitutions in proteins is to modulate protein-protein interactions (PPIs). To interpret such variants at the molecular level and to obtain useful information for prediction purposes, it ... ...

Abstract	One of the known potential effects of disease-causing amino acid substitutions in proteins is to modulate protein-protein interactions (PPIs). To interpret such variants at the molecular level and to obtain useful information for prediction purposes, it is important to determine whether they are located at protein-protein interfaces, which are composed of two main regions, core and rim, with different evolutionary conservation and physicochemical properties. Here we have performed a structural, energetics and computational analysis of interactions between proteins hosting mutations related to diseases detected in newborn screening. Interface residues were classified as core or rim, showing that the core residues contribute the most to the binding free energy of the PPI. Disease-causing variants are more likely to occur at the interface core region rather than at the interface rim (
MeSH term(s)	Amino Acid Sequence ; Amino Acid Substitution ; Computational Biology/methods ; Disease/genetics ; Humans ; Infant, Newborn ; Infant, Newborn, Diseases/diagnosis ; Infant, Newborn, Diseases/genetics ; Molecular Docking Simulation ; Mutant Proteins/chemistry ; Mutation/genetics ; Neonatal Screening ; Protein Binding ; Protein Subunits/chemistry ; Proteins/genetics ; beta-Globins/chemistry
Chemical Substances	Mutant Proteins ; Protein Subunits ; Proteins ; beta-Globins
Language	English
Publishing date	2019-03-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms20071583
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Article ; Online: Structural Prediction of Protein-Protein Interactions by Docking: Application to Biomedical Problems.

Barradas-Bautista, Didier / Rosell, Mireia / Pallara, Chiara / Fernández-Recio, Juan

Advances in protein chemistry and structural biology

2017 Volume 110, Page(s) 203–249

Abstract: A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is ... ...

Abstract	A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein-protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein-protein interactions, or providing modeled structural data for drug discovery targeting protein-protein interactions.
MeSH term(s)	Biomedical Research ; Computational Biology ; Drug Discovery ; Humans ; Models, Molecular ; Molecular Docking Simulation ; Protein Binding ; Proteins/chemistry ; Proteins/metabolism
Chemical Substances	Proteins
Language	English
Publishing date	2017-08-31
Publishing country	Netherlands
Document type	Journal Article ; Review
ISSN	1876-1631 ; 1876-1623
ISSN (online)	1876-1631
ISSN	1876-1623
DOI	10.1016/bs.apcsb.2017.06.003
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