RESUMO
Metals are present in >30% of proteins found in nature and assist them to perform important biological functions, including storage, transport, signal transduction and enzymatic activity. Traditional and experimental techniques for metal-binding site prediction are usually costly and time-consuming, making computational tools that can assist in these predictions of significant importance. Here we present Genetic Active Site Search (GASS)-Metal, a new method for protein metal-binding site prediction. The method relies on a parallel genetic algorithm to find candidate metal-binding sites that are structurally similar to curated templates from M-CSA and MetalPDB. GASS-Metal was thoroughly validated using homologous proteins and conservative mutations of residues, showing a robust performance. The ability of GASS-Metal to identify metal-binding sites was also compared with state-of-the-art methods, outperforming similar methods and achieving an MCC of up to 0.57 and detecting up to 96.1% of the sites correctly. GASS-Metal is freely available at https://gassmetal.unifei.edu.br. The GASS-Metal source code is available at https://github.com/sandroizidoro/gassmetal-local.
Assuntos
Proteínas , Software , Algoritmos , Sítios de Ligação , Domínio Catalítico , Metais/química , Metais/metabolismo , Proteínas/químicaRESUMO
MOTIVATION: G protein-coupled receptors (GPCRs) can selectively bind to many types of ligands, ranging from light-sensitive compounds, ions, hormones, pheromones and neurotransmitters, modulating cell physiology. Considering their role in many essential cellular processes, they are one of the most targeted protein families, with over a third of all approved drugs modulating GPCR signalling. Despite this, the large diversity of receptors and their multipass transmembrane architectures make the identification and development of novel specific, and safe GPCR ligands a challenge. While computational approaches have the potential to assist GPCR drug development, they have presented limited performance and generalization capabilities. Here, we explored the use of graph-based signatures to develop pdCSM-GPCR, a method capable of rapidly and accurately screening potential GPCR ligands. RESULTS: Bioactivity data (IC50, EC50, Ki and Kd) for individual GPCRs were curated. After curation, we used the data for developing predictive models for 36 major GPCR targets, across 4 classes (A, B, C and F). Our models compose the most comprehensive computational resource for GPCR bioactivity prediction to date. Across stratified 10-fold cross-validation and blind tests, our approach achieved Pearson's correlations of up to 0.89, significantly outperforming previous methods. Interpreting our results, we identified common important features of potent GPCRs ligands, which tend to have bicyclic rings, leading to higher levels of aromaticity. We believe pdCSM-GPCR will be an invaluable tool to assist screening efforts, enriching compound libraries and ranking candidates for further experimental validation. AVAILABILITY AND IMPLEMENTATION: pdCSM-GPCR predictive models and datasets used have been made available via a freely accessible and easy-to-use web server at http://biosig.unimelb.edu.au/pdcsm_gpcr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.
RESUMO
Despite all efforts to provide new chemical entities to tackle leishmaniases, we are still dependent on a the limited drug arsenal, together with drawbacks like toxicity and drug-resistant parasites. Collaborative drug discovery emerged as an option to speed up the way to find alternative antileishmanial agents. This is the case of Medicines for Malaria Ventures - MMV, that promotes an open source drug discovery initiative to fight diseases worldwide. Here, we screened 400 compounds from 'Pathogen Box' (PBox) collection against Leishmania braziliensis, the main etiological agent of cutaneous leishmaniasis in Brazil. Twenty-three compounds were able to inhibit ≥ 80 % L. braziliensis growth at 5 µM. Six out of the PBox selected 23 compounds were found to be highly selective against L. braziliensis intracellular amastigotes with selectivity index varying from > 104 to > 746 and IC50s ranging from 47 to 480 nM. The compounds were also active against antimony-resistant L. braziliensis isolated from the field or laboratory selected mutants, revealing the potential on treating patients infected with drug resistant parasites. Most of the selected compounds were known to be active against kinetoplastids, however, two compounds (MMV688703 and MMV676477) were part of toxoplasmosis and tuberculosis 'PBox' disease set, reinforcing the potential of phenotyping screening to unveil drug repurposing. Here we applied a computational prediction of pharmacokinetic properties using the ADMET predictor pkCSM (http://biosig.unimelb.edu.au/pkcsm/). The tool offered clues on potential drug development needs and can support further in vivo studies. Molecular docking analysis identified CRK3 (LbrM.35.0660), CYP450 (LbrM.30.3580) and PKA (LbrM.18.1180) as L. braziliensis targets for MMV676604, MMV688372 and MMV688703, respectively. Compounds from 'Pathogen Box' thus represents a new hope for novel (or repurposed) small molecules source to tackle leishmaniases.
Assuntos
Antimônio/farmacologia , Antiprotozoários/farmacologia , Descoberta de Drogas , Reposicionamento de Medicamentos , Resistência a Medicamentos , Leishmania braziliensis/efeitos dos fármacos , Bibliotecas de Moléculas Pequenas , Animais , Antiprotozoários/química , Antiprotozoários/farmacocinética , Antiprotozoários/toxicidade , Simulação por Computador , Relação Dose-Resposta a Droga , Humanos , Concentração Inibidora 50 , Leishmania braziliensis/crescimento & desenvolvimento , Modelos Biológicos , Modelos Químicos , Estrutura Molecular , Testes de Sensibilidade Parasitária , Células THP-1RESUMO
The ability to predict how mutations affect protein structure, folding, and flexibility can elucidate the molecular mechanisms leading to disruption of supersecondary structures, the emergence of phenotypes, as well guiding rational protein engineering. The advent of fast and accurate computational tools has enabled us to comprehensively explore the landscape of mutation effects on protein structures, prioritizing mutations for rational experimental validation.Here we describe the use of two complementary web-based in silico methods, DUET and DynaMut, developed to infer the effects of mutations on folding, stability, and flexibility and how they can be used to explore and interpret these effects on protein supersecondary structures.
Assuntos
Substituição de Aminoácidos/genética , Biologia Computacional/métodos , Engenharia de Proteínas/métodos , Proteínas/química , Motivos de Aminoácidos , Humanos , Mutação de Sentido Incorreto/genética , Dobramento de Proteína , Estabilidade Proteica , Proteínas/genéticaRESUMO
A crucial factor for the approval and success of any drug is how it behaves in the body. Many drugs, however, do not reach the market due to poor efficacy or unacceptable side effects. It is therefore important to take these into consideration early in the drug development process, both in the prioritization of potential hits, and optimization of lead compounds. In silico approaches offer a cost and time-effective approach to rapidly screen and optimize pharmacokinetic and toxicity properties. Here we demonstrate the use of the comprehensive analysis system pkCSM, to allow early identification of potential problems, prioritization of hits, and optimization of leads.
Assuntos
Biologia Computacional/métodos , Bibliotecas de Moléculas Pequenas/farmacocinética , Bibliotecas de Moléculas Pequenas/toxicidade , Células CACO-2 , Simulação por Computador , Desenho de Fármacos , Humanos , LigantesRESUMO
INTRODUCTION: Mutations introduce diversity into genomes, leading to selective changes and driving evolution. These changes have contributed to the emergence of many of the current major health concerns of the 21st century, from the development of genetic diseases and cancers to the rise and spread of drug resistance. The experimental systematic testing of all mutations in a system of interest is impractical and not cost-effective, which has created interest in the development of computational tools to understand the molecular consequences of mutations to aid and guide rational experimentation. Areas covered: Here, the authors discuss the recent development of computational methods to understand the effects of coding mutations to protein function and interactions, particularly in the context of the 3D structure of the protein. Expert opinion: While significant progress has been made in terms of innovative tools to understand and quantify the different range of effects in which a mutation or a set of mutations can give rise to a phenotype, a great gap still exists when integrating these predictions and drawing causality conclusions linking variants. This often requires a detailed understanding of the system being perturbed. However, as part of the drug development process it can be used preemptively in a similar fashion to pharmacokinetics predictions, to guide development of therapeutics to help guide the design and analysis of clinical trials, patient treatment and public health policy strategies.
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Desenho de Fármacos , Doenças Genéticas Inatas/genética , Neoplasias/tratamento farmacológico , Ensaios Clínicos como Assunto/métodos , Desenho Assistido por Computador , Resistência a Medicamentos/genética , Política de Saúde , Humanos , Mutação , Neoplasias/genética , Conformação ProteicaRESUMO
Over the past two decades, several computational methods have been proposed to predict how missense mutations can affect protein structure and function, either by altering protein stability or interactions with its partners, shedding light into potential molecular mechanisms giving rise to different phenotypes. Effectively and efficiently predicting consequences of mutations on protein-nucleic acid interactions, however, remained until recently a great and unmet challenge. Here we report an updated webserver for mCSM-NA, the only scalable method we are aware of capable of quantitatively predicting the effects of mutations in protein coding regions on nucleic acid binding affinities. We have significantly enhanced the original method by including a pharmacophore modelling and information of nucleic acid properties into our graph-based signatures, considering the reverse mutation and by using a refined, more reliable data set, based on a new release of the ProNIT database, which has significantly improved the reliability and applicability of the methodology. Our new predictive model was capable of achieving a correlation coefficient of up to 0.70 on cross-validation and 0.68 on blind-tests, outperforming its previous version. The server is freely available via a user-friendly web interface at: http://structure.bioc.cam.ac.uk/mcsm_na.
Assuntos
Proteínas de Ligação a DNA/química , DNA/química , Modelos Estatísticos , Mutação de Sentido Incorreto , Proteínas de Ligação a RNA/química , Software , Sítios de Ligação , DNA/genética , DNA/metabolismo , Proteínas de Ligação a DNA/genética , Proteínas de Ligação a DNA/metabolismo , Humanos , Internet , Aprendizado de Máquina , Modelos Moleculares , Ligação Proteica , Conformação Proteica em alfa-Hélice , Conformação Proteica em Folha beta , Domínios e Motivos de Interação entre Proteínas , Estabilidade Proteica , Proteínas de Ligação a RNA/genética , Proteínas de Ligação a RNA/metabolismo , Genética Reversa , TermodinâmicaRESUMO
BACKGROUND: The human malaria parasite Plasmodium vivax infects red blood cells through a key pathway that requires interaction between Duffy binding protein II (DBPII) and its receptor on reticulocytes, the Duffy antigen/receptor for chemokines (DARC). A high proportion of P. vivax-exposed individuals fail to develop antibodies that inhibit DBPII-DARC interaction, and genetic factors that modulate this humoral immune response are poorly characterized. Here, we investigate if DBPII responsiveness could be HLA class II-linked. METHODOLOGY/PRINCIPAL FINDINGS: A community-based open cohort study was carried out in an agricultural settlement of the Brazilian Amazon, in which 336 unrelated volunteers were genotyped for HLA class II (DRB1, DQA1 and DQB1 loci), and their DBPII immune responses were monitored over time (baseline, 6 and 12 months) by conventional serology (DBPII IgG ELISA-detected) and functional assays (inhibition of DBPII-erythrocyte binding). The results demonstrated an increased susceptibility of the DRB1*13:01 carriers to develop and sustain an anti-DBPII IgG response, while individuals with the haplotype DRB1*14:02-DQA1*05:03-DQB1*03:01 were persistent non-responders. HLA class II gene polymorphisms also influenced the functional properties of DBPII antibodies (BIAbs, binding inhibitory antibodies), with three alleles (DRB1*07:01, DQA1*02:01 and DQB1*02:02) comprising a single haplotype linked with the presence and persistence of the BIAbs response. Modelling the structural effects of the HLA-DRB1 variants revealed a number of differences in the peptide-binding groove, which is likely to lead to altered antigen binding and presentation profiles, and hence may explain the differences in subject responses. CONCLUSIONS/SIGNIFICANCE: The current study confirms the heritability of the DBPII antibody response, with genetic variation in HLA class II genes influencing both the development and persistence of IgG antibody responses. Cellular studies to increase knowledge of the binding affinities of DBPII peptides for class II molecules linked with good or poor antibody responses might lead to the development of strategies for controlling the type of helper T cells activated in response to DBPII.
Assuntos
Anticorpos Antiprotozoários/imunologia , Antígenos de Protozoários/imunologia , Cadeias HLA-DRB1/imunologia , Malária Vivax/genética , Malária Vivax/imunologia , Plasmodium vivax/imunologia , Proteínas de Protozoários/imunologia , Receptores de Superfície Celular/imunologia , Adulto , Alelos , Anticorpos Antiprotozoários/sangue , Anticorpos Antiprotozoários/metabolismo , Brasil/epidemiologia , Proteínas de Transporte/genética , Estudos de Coortes , Sistema do Grupo Sanguíneo Duffy/imunologia , Ensaio de Imunoadsorção Enzimática , Eritrócitos/parasitologia , Feminino , Variação Genética , Genótipo , Cadeias HLA-DRB1/genética , Haplótipos , Humanos , Imunoglobulina G/imunologia , Malária Vivax/epidemiologia , Malária Vivax/parasitologia , Masculino , Pessoa de Meia-Idade , Plasmodium vivax/química , Plasmodium vivax/genética , Polimorfismo GenéticoRESUMO
Determining the affinity of a ligand for a given protein is a crucial component of drug development and understanding their biological effects. Predicting binding affinities is a challenging and difficult task, and despite being regarded as poorly predictive, scoring functions play an important role in the analysis of molecular docking results. Here, we present CSM-Lig (http://structure.bioc.cam.ac.uk/csm_lig), a web server tailored to predict the binding affinity of a protein-small molecule complex, encompassing both protein and small-molecule complementarity in terms of shape and chemistry via graph-based structural signatures. CSM-Lig was trained and evaluated on different releases of the PDBbind databases, achieving a correlation of up to 0.86 on 10-fold cross validation and 0.80 in blind tests, performing as well as or better than other widely used methods. The web server allows users to rapidly and automatically predict binding affinities of collections of structures and assess the interactions made. We believe CSM-lig would be an invaluable tool for helping assess docking poses, the effects of multiple mutations, including insertions, deletions and alternative splicing events, in protein-small molecule affinity, unraveling important aspects that drive protein-compound recognition.
Assuntos
Internet , Ligantes , Proteínas/química , Bibliotecas de Moléculas Pequenas/química , Software , Bases de Dados de Proteínas , Conjuntos de Dados como Assunto , Simulação de Acoplamento Molecular , Mutação , Ligação Proteica , Proteínas/genética , Reprodutibilidade dos Testes , Bibliotecas de Moléculas Pequenas/farmacologia , Especificidade por Substrato , Interface Usuário-ComputadorRESUMO
Computational methods have traditionally struggled to predict the effect of mutations in antibody-antigen complexes on binding affinity. This has limited their usefulness during antibody engineering and development, and their ability to predict biologically relevant escape mutations. Here we present mCSM-AB, a user-friendly web server for accurately predicting antibody-antigen affinity changes upon mutation which relies on graph-based signatures. We show that mCSM-AB performs better than comparable methods that have been previously used for antibody engineering. mCSM-AB web server is available at http://structure.bioc.cam.ac.uk/mcsm_ab.