RESUMEN

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.

Asunto(s)

Sitios de Unión , Modelos Moleculares , Unión Proteica , Dominios y Motivos de Interacción de Proteínas , Proteínas , Biología Computacional , Ligandos , Simulación del Acoplamiento Molecular , Conformación Proteica , Proteínas/química , Proteínas/metabolismo , Programas Informáticos

RESUMEN

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.

RESUMEN

The infectious coronavirus disease (COVID-19) pandemic, caused by the coronavirus SARS-CoV-2, appeared in December 2019 in Wuhan, China, and has spread worldwide. As of today, more than 46 million people have been infected and over 1.2 million fatalities. With the purpose of contributing to the development of effective therapeutics, we performed an in silico determination of binding hot-spots and an assessment of their druggability within the complete SARS-CoV-2 proteome. All structural, non-structural, and accessory proteins have been studied, and whenever experimental structural data of SARS-CoV-2 proteins were not available, homology models were built based on solved SARS-CoV structures. Several potential allosteric or protein-protein interaction druggable sites on different viral targets were identified, knowledge that could be used to expand current drug discovery endeavors beyond the currently explored cysteine proteases and the polymerase complex. It is our hope that this study will support the efforts of the scientific community both in understanding the molecular determinants of this disease and in widening the repertoire of viral targets in the quest for repurposed or novel drugs against COVID-19.

Asunto(s)

Modelos Moleculares , Proteoma , SARS-CoV-2/metabolismo , Proteínas Virales/metabolismo , Antivirales/uso terapéutico , Sitios de Unión , Descubrimiento de Drogas , Humanos , SARS-CoV-2/patogenicidad , Tratamiento Farmacológico de COVID-19

RESUMEN

Rational drug discovery strategy requires a design of small molecules as candidate drugs which can specifically inhibit a target protein or any other macromolecule and effectively interfere in a defined physiological process. One of the important bacterial protein targets aimed toward developing new antibiotics is peptidyl-tRNA hydrolase (Pth). The discovery that cytarabine, a known anticancer drug, binds to Pth from Acinetobacter baumannii in a cleft located away from the catalytic site of this enzyme, published in Biochemical Journal, opens up interesting new avenues for drug design. An approach involving crystallographic identification of multiple ligand-binding sites on a target protein surface could enable iterative optimization of multiple high-affinity ligands, which may synergistically interfere in the target function with enhanced effect.

Asunto(s)

Antibacterianos , Diseño de Fármacos , Acinetobacter baumannii , Sitios de Unión , Hidrolasas de Éster Carboxílico , Ligandos , Modelos Moleculares

RESUMEN

The fast Fourier transform (FFT) sampling algorithm has been used with success in application to protein-protein docking and for protein mapping, the latter docking a variety of small organic molecules for the identification of binding hot spots on the target protein. Here we explore the local rather than global usage of the FFT sampling approach in docking applications. If the global FFT based search yields a near-native cluster of docked structures for a protein complex, then focused resampling of the cluster generally leads to a substantial increase in the number of conformations close to the native structure. In protein mapping, focused resampling of the selected hot spot regions generally reveals further hot spots that, while not as strong as the primary hot spots, also contribute to ligand binding. The detection of additional ligand binding regions is shown by the improved overlap between hot spots and bound ligands.

Asunto(s)

Análisis de Fourier , Simulación del Acoplamiento Molecular , Proteínas/química , Algoritmos , Ligandos , Conformación Proteica

RESUMEN

Interference with protein-protein interactions of interfaces larger than 1500 Ų by small drug-like molecules is notoriously difficult, particularly if targeting homodimers. The tRNA modifying enzyme Tgt is only functionally active as a homodimer. Thus, blocking Tgt dimerization is a promising strategy for drug therapy as this protein is key to the development of Shigellosis. Our goal was to identify hot-spot residues which, upon mutation, result in a predominantly monomeric state of Tgt. The detailed understanding of the spatial location and stability contribution of the individual interaction hot-spot residues and the plasticity of motifs involved in the interface formation is a crucial prerequisite for the rational identification of drug-like inhibitors addressing the respective dimerization interface. Using computational analyses, we identified hot-spot residues that contribute particularly to dimer stability: a cluster of hydrophobic and aromatic residues as well as several salt bridges. This in silico prediction led to the identification of a promising double mutant, which was validated experimentally. Native nano-ESI mass spectrometry showed that the dimerization of the suggested mutant is largely prevented resulting in a predominantly monomeric state. Crystal structure analysis and enzyme kinetics of the mutant variant further support the evidence for enhanced monomerization and provide first insights into the structural consequences of the dimer destabilization.

Asunto(s)

Modelos Moleculares , Proteínas Mutantes/química , Pentosiltransferasa/química , ARN de Transferencia/metabolismo , Sustitución de Aminoácidos , Proteínas Arqueales/química , Proteínas Arqueales/genética , Proteínas Arqueales/metabolismo , Proteínas Bacterianas/química , Proteínas Bacterianas/genética , Proteínas Bacterianas/metabolismo , Biología Computacional , Bases de Datos de Proteínas , Dimerización , Estabilidad de Enzimas , Proteínas de Escherichia coli/química , Proteínas de Escherichia coli/genética , Proteínas de Escherichia coli/metabolismo , Sistemas Especialistas , Isoenzimas/química , Isoenzimas/genética , Isoenzimas/metabolismo , Cinética , Simulación de Dinámica Molecular , Mutagénesis Sitio-Dirigida , Proteínas Mutantes/metabolismo , Pentosiltransferasa/genética , Pentosiltransferasa/metabolismo , Conformación Proteica , Dominios y Motivos de Interacción de Proteínas , Proteínas Recombinantes/química , Proteínas Recombinantes/metabolismo

RESUMEN

An exhaustive computational exercise on a comprehensive set of 15 therapeutic kinase inhibitors was undertaken to identify as to which compounds hit which kinase off-targets in the human kinome. Although the kinase selectivity propensity of each inhibitor against ∼480 kinase targets is predicted, we compared our predictions to ∼280 kinase targets for which consistent experimental data are available and demonstrate an overall average prediction accuracy and specificity of ∼90%. A comparison of the predictions was extended to an additional ∼60 kinases for sorafenib and sunitinib as new experimental data were reported recently with similar prediction accuracy. The successful predictive capabilities allowed us to propose predictions on the remaining kinome targets in an effort to repurpose known kinase inhibitors to these new kinase targets that could hold therapeutic potential.