RESUMO
The human FoxP transcription factors dimerize via three-dimensional domain swapping, a unique feature among the human Fox family, as result of evolutionary sequence adaptations in the forkhead domain. This is the case for the conserved glycine and proline residues in the wing 1 region, which are absent in FoxP proteins but present in most of the Fox family. In this work, we engineered both glycine (G) and proline-glycine (PG) insertion mutants to evaluate the deletion events in FoxP proteins in their dimerization, stability, flexibility, and DNA-binding ability. We show that the PG insertion only increases protein stability, whereas the single glycine insertion decreases the association rate and protein stability and promotes affinity to the DNA ligand.
Assuntos
Fatores de Transcrição Forkhead , Glicina , Prolina , Proteínas Repressoras , Deleção de Sequência , Humanos , Fatores de Transcrição Forkhead/genética , Fatores de Transcrição Forkhead/metabolismo , Fatores de Transcrição Forkhead/química , Prolina/genética , Prolina/metabolismo , Prolina/química , Glicina/metabolismo , Glicina/genética , Glicina/química , Proteínas Repressoras/genética , Proteínas Repressoras/metabolismo , Proteínas Repressoras/química , Domínios Proteicos , Evolução Molecular , Estabilidade Proteica , Multimerização Proteica , DNA/metabolismo , DNA/genética , DNA/química , Ligação Proteica , Sequência de AminoácidosRESUMO
This study investigated the potential of selected compounds as inhibitors of SARS-CoV-2 Mpro through pharmacokinetic and toxicological analyses, molecular docking, and molecular dynamics simulations. In silico molecular docking simulations revealed promising ligands with favorable binding affinities for Mpro, ranging from -6.2 to -9.5 kcal/mol. Moreover, molecular dynamics simulations demonstrated the stability of protein-ligand complexes over 200 ns, maintaining protein secondary structures. MM-PBSA analysis revealed favorable interactions between ligands and Mpro, with negative binding energy values. Hydrogen bond formation capacity during molecular dynamics was confirmed, indicating consistent interactions with Mpro catalytic residues. Based on these findings, selected ligands show promise for future studies in developing COVID-19 treatments.
Assuntos
Tratamento Farmacológico da COVID-19 , Proteases 3C de Coronavírus , Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , SARS-CoV-2 , SARS-CoV-2/efeitos dos fármacos , Humanos , Proteases 3C de Coronavírus/antagonistas & inibidores , Proteases 3C de Coronavírus/química , Proteases 3C de Coronavírus/metabolismo , Antivirais/química , Antivirais/farmacologia , Inibidores de Proteases/química , Inibidores de Proteases/farmacologia , Ligação de Hidrogênio , Ligantes , COVID-19/virologia , Ligação ProteicaRESUMO
Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.
Assuntos
Aprendizado de Máquina , Proteínas , Proteínas/química , Proteínas/metabolismo , Ligantes , Software , Simulação de Acoplamento MolecularRESUMO
Genome-wide association studies (GWAS) have mapped over 90 % of disease- or trait-associated variants within the non-coding genome, like cis-regulatory elements (CREs). Non-coding single nucleotide polymorphisms (SNPs) are genomic variants that can change how DNA-binding regulatory proteins, like transcription factors (TFs), interact with the genome and regulate gene expression. NKX2-5 is a TF essential for proper heart development, and mutations affecting its function have been associated with congenital heart diseases (CHDs). However, establishing a causal mechanism between non-coding genomic variants and human disease remains challenging. To address this challenge, we identified 8475 SNPs predicted to alter NKX2-5 DNA-binding using a position weight matrix (PWM)-based predictive model. Five variants were prioritized for in vitro validation; four of them are associated with traits and diseases that impact cardiovascular health. The impact of these variants on NKX2-5 binding was evaluated with electrophoretic mobility shift assay (EMSA) using purified recombinant NKX2-5 homeodomain. Binding curves were constructed to determine changes in binding between variant and reference alleles. Variants rs7350789, rs7719885, rs747334, and rs3892630 increased binding affinity, whereas rs61216514 decreased binding by NKX2-5 when compared to the reference genome. Our findings suggest that differential TF-DNA binding affinity can be key in establishing a causal mechanism of pathogenic variants.
Assuntos
Estudo de Associação Genômica Ampla , Fatores de Transcrição , Humanos , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismo , Proteínas de Ligação a DNA/metabolismo , Sequências Reguladoras de Ácido Nucleico , DNA/genética , Proteína Homeobox Nkx-2.5/genéticaRESUMO
OBJECTIVE: To evaluate the effect of intravenous (IV) ceftriaxone on free bilirubin concentrations in infants with unconjugated hyperbilirubinemia born at term. STUDY DESIGN: A prospective study was performed with subjects serving as their own controls. Our inclusion criteria were infants born at term <7 days old with sepsis and receiving IV antibiotics for >3 days and resolving hyperbilirubinemia with total serum bilirubin levels between 6 and12 mg/dL by day 4 of life. Free bilirubin concentrations were measured by the peroxidase method using a UB analyzer and a Zone Fluidics device before (baseline) and 15 minutes after (follow-up) IV ceftriaxone administration on postnatal days 4 to 6. Paired measurements of free bilirubin were analyzed using a Student paired t-test or Wilcoxon signed-rank test. RESULTS: In total, 27 infants were studied. The mean free bilirubin (µg/dL) at follow-up was not different from that at baseline when measured by the UB analyzer (P = .78). The mean free bilirubin was significantly lower at follow-up compared with baseline when measured by the Zone Fluidics device (P = .02). The ratio of a free bilirubin with and without ceftriaxone, an index of displacing effect, was 1.02 (95% CI 0.89-1.14) using the UB analyzer and 0.58 (95% CI 0.30-0.86) using the Zone Fluidics device. CONCLUSIONS: Ceftriaxone is not associated with a bilirubin-displacing effect in infants with a mild unconjugated hyperbilirubinemia. Home therapy with once-daily intramuscular ceftriaxone may be an alternative option for management of sepsis in asymptomatic infants with a mild unconjugated hyperbilirubinemia born at term.
Assuntos
Bilirrubina , Sepse , Humanos , Lactente , Ceftriaxona/uso terapêutico , Estudos Prospectivos , Hiperbilirrubinemia/tratamento farmacológicoRESUMO
CD8+ T-cells play a crucial role in the control of HIV replication. HIV-specific CD8+ T-cell responses rapidly expand since the acute phase of the infection, and it has been observed that HIV controllers harbor CD8+ T-cells with potent anti-HIV capacity. The development of CD8+ T-cell-based vaccine against HIV-1 has focused on searching for immunodominant epitopes. However, the strong immune pressure of CD8+ T-cells causes the selection of viral variants with mutations in immunodominant epitopes. Since HIV-1 mutations are selected under the context of a specific HLA-I, the circulation of viral variants with these mutations is highly predictable based on the most prevalent HLA-I within a population. We previously demonstrated the adaptation of circulating strains of HIV-1 to the HLA-A*02 molecule by identifying mutations under positive selection located in GC9 and SL9 epitopes derived from the Gag protein. Also, we used an in silico prediction approach and evaluated whether the mutations found had a higher or lower affinity to the HLA-A*02. Although this strategy allowed predicting the interaction between mutated peptides and HLA-I, the functional response of CD8+ T-cells that these peptides induce is unknown. In the present work, peripheral blood mononuclear cells from 12 HIV-1+ HLA-A*02:01+ individuals were stimulated with the mutated and wild-type peptides derived from the GC9 and SL9 epitopes. The functional profile of CD8+ T-cells was evaluated using flow cytometry, and the frequency of subpopulations was determined according to their number of functions and the polyfunctionality index. The results suggest that the quality of the response (polyfunctionality) could be associated with the binding affinity of the peptide to the HLA molecule, and the functional profile of specific CD8+ T-cells to mutated epitopes in individuals under cART is maintained.
Assuntos
Infecções por HIV , Soropositividade para HIV , HIV-1 , Linfócitos T CD8-Positivos , Colômbia , Epitopos , Produtos do Gene gag , Antígenos HLA-A , Humanos , Epitopos Imunodominantes , Leucócitos Mononucleares , PeptídeosRESUMO
The ongoing coronavirus 2019 (COVID-19) pandemic, triggered by the emerging SARS-CoV-2 virus, represents a global public health challenge. Therefore, the development of effective vaccines is an urgent need to prevent and control virus spread. One of the vaccine production strategies uses the in silico epitope prediction from the virus genome by immunoinformatic approaches, which assist in selecting candidate epitopes for in vitro and clinical trials research. This study introduces the EpiCurator workflow to predict and prioritize epitopes from SARS-CoV-2 genomes by combining a series of computational filtering tools. To validate the workflow effectiveness, SARS-CoV-2 genomes retrieved from the GISAID database were analyzed. We identified 11 epitopes in the receptor-binding domain (RBD) of Spike glycoprotein, an important antigenic determinant, not previously described in the literature or published on the Immune Epitope Database (IEDB). Interestingly, these epitopes have a combination of important properties: recognized in sequences of the current variants of concern, present high antigenicity, conservancy, and broad population coverage. The RBD epitopes were the source for a multi-epitope design to in silico validation of their immunogenic potential. The multi-epitope overall quality was computationally validated, endorsing its efficiency to trigger an effective immune response since it has stability, high antigenicity and strong interactions with Toll-Like Receptors (TLR). Taken together, the findings in the current study demonstrated the efficacy of the workflow for epitopes discovery, providing target candidates for immunogen development.
RESUMO
All biological processes rely on the formation of protein-ligand, protein-peptide and protein-protein complexes. Studying the affinity, kinetics and thermodynamics of binding between these pairs is critical for understanding basic cellular mechanisms. Many different technologies have been designed for probing interactions between biomolecules, each based on measuring different signals (fluorescence, heat, thermophoresis, scattering and interference, among others). Evaluation of the data from binding experiments and their fitting is an essential step towards the quantification of binding affinities. Here, user-friendly online tools to analyze biophysical data from steady-state fluorescence spectroscopy, microscale thermophoresis and differential scanning fluorimetry experiments are presented. The modules of the data-analysis platform (https://spc.embl-hamburg.de/) contain classical thermodynamic models and clear user guidelines for the determination of equilibrium dissociation constants (Kd) and thermal unfolding parameters such as melting temperatures (Tm).
Assuntos
Proteínas Quinases Dependentes de GMP Cíclico/química , Proteínas Quinases Dependentes de GMP Cíclico/metabolismo , Fluorescência , Mycobacterium tuberculosis/metabolismo , Sistemas On-Line , Temperatura , Termodinâmica , Cinética , Ligantes , Ligação Proteica , Espectrometria de FluorescênciaRESUMO
Ribosome biogenesis in eukaryotes requires the participation of several transactivation factors that are involved in the modification, assembly, transport and quality control of the ribosomal subunits. One of these factors is the Large subunit GTPase 1 (Lsg1), a protein that acts as the release factor for the export adaptor named Nonsense-mediated mRNA decay 3 protein (Nmd3) and facilitates the incorporation of the last structural protein uL16 into the 60S subunit. Here, we characterised the recombinant yeast Lsg1 and studied its catalysis and binding properties for guanine nucleotides. We described the interaction of Lsg1 with guanine nucleotides alone and in the presence of the complex Nmd3â¢60S using fluorescence spectroscopy. Lsg1 has a greater affinity for GTP than for GDP suggesting that in the cell cytoplasm it exists mainly bound to the former. In the presence of 60S subunits loaded with Nmd3, the affinity of Lsg1 for both nucleotides increases but to a larger extent towards GTP. From this observation together with the excess of GTP present in the cytoplasm of exponentially growing cells over that of GDP, we can infer that the pre-ribosomal particle composed by Nmd3â¢60S acts as a GTP Stabilising Factor for Lsg1. Additionally, Lsg1 undergoes different conformational changes depending on its binding partner or the guanine nucleotides it interacts with. Steady-state kinetic analysis of free Lsg1 indicated slow GTP hydrolysis with values of kcat 1â¯min-1 and Km of 34⯵M.
Assuntos
Proteínas de Ligação ao GTP/metabolismo , Guanosina Difosfato/metabolismo , Guanosina Trifosfato/metabolismo , Proteínas de Ligação a RNA/metabolismo , Proteínas Ribossômicas/metabolismo , Proteínas de Saccharomyces cerevisiae/metabolismo , Saccharomyces cerevisiae/enzimologia , Sítios de Ligação , Clonagem Molecular , Escherichia coli/genética , Escherichia coli/metabolismo , Proteínas de Ligação ao GTP/química , Proteínas de Ligação ao GTP/genética , Expressão Gênica , Vetores Genéticos/química , Vetores Genéticos/metabolismo , Guanosina Difosfato/química , Guanosina Trifosfato/química , Cinética , Proteínas Ligantes de Maltose/química , Proteínas Ligantes de Maltose/genética , Proteínas Ligantes de Maltose/metabolismo , Modelos Moleculares , Ligação Proteica , Conformação Proteica em alfa-Hélice , Conformação Proteica em Folha beta , Domínios e Motivos de Interação entre Proteínas , Estrutura Terciária de Proteína , Proteínas de Ligação a RNA/química , Proteínas de Ligação a RNA/genética , Proteínas Recombinantes de Fusão/química , Proteínas Recombinantes de Fusão/genética , Proteínas Recombinantes de Fusão/metabolismo , Proteínas Ribossômicas/química , Proteínas Ribossômicas/genética , Subunidades Ribossômicas Maiores de Eucariotos/enzimologia , Subunidades Ribossômicas Maiores de Eucariotos/genética , Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/química , Proteínas de Saccharomyces cerevisiae/genética , Especificidade por Substrato , TermodinâmicaRESUMO
BACKGROUND: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. OBJECTIVE: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. METHODS: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions. RESULTS: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. CONCLUSION: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina.
Assuntos
Aprendizado de Máquina , Humanos , Ligantes , Simulação de Acoplamento Molecular , Ligação Proteica , TermodinâmicaRESUMO
The COVID-19 pandemic has spread worldwide. However, as soon as the first vaccines-the only scientifically verified and efficient therapeutic option thus far-were released, mutations combined into variants of SARS-CoV-2 that are more transmissible and virulent emerged, raising doubts about their efficiency. This study aims to explain possible molecular mechanisms responsible for the increased transmissibility and the increased rate of hospitalizations related to the new variants. A combination of theoretical methods was employed. Constant-pH Monte Carlo simulations were carried out to quantify the stability of several spike trimeric structures at different conformational states and the free energy of interactions between the receptor-binding domain (RBD) and angiotensin-converting enzyme II (ACE2) for the most worrying variants. Electrostatic epitopes were mapped using the PROCEEDpKa method. These analyses showed that the increased virulence is more likely to be due to the improved stability to the S trimer in the opened state, in which the virus can interact with the cellular receptor, ACE2, rather than due to alterations in the complexation RBD-ACE2, since the difference observed in the free energy values was small (although more attractive in general). Conversely, the South African/Beta variant (B.1.351), compared with the SARS-CoV-2 wild type (wt), is much more stable in the opened state with one or two RBDs in the up position than in the closed state with three RBDs in the down position favoring the infection. Such results contribute to understanding the natural history of disease and indicate possible strategies for developing new therapeutic molecules and adjusting the vaccine doses for higher B-cell antibody production.
RESUMO
A new betacoronavirus named SARS-CoV-2 has emerged as a new threat to global health and economy. A promising target for both diagnosis and therapeutics treatments of the new disease named COVID-19 is the coronavirus (CoV) spike (S) glycoprotein. By constant-pH Monte Carlo simulations and the PROCEEDpKa method, we have mapped the electrostatic epitopes for four monoclonal antibodies and the angiotensin-converting enzyme 2 (ACE2) on both SARS-CoV-1 and the new SARS-CoV-2 S receptor binding domain (RBD) proteins. We also calculated free energy of interactions and shown that the S RBD proteins from both SARS viruses binds to ACE2 with similar affinities. However, the affinity between the S RBD protein from the new SARS-CoV-2 and ACE2 is higher than for any studied antibody previously found complexed with SARS-CoV-1. Based on physical chemical analysis and free energies estimates, we can shed some light on the involved molecular recognition processes, their clinical aspects, the implications for drug developments, and suggest structural modifications on the CR3022 antibody that would improve its binding affinities for SARS-CoV-2 and contribute to address the ongoing international health crisis.
Assuntos
Betacoronavirus/química , Peptidil Dipeptidase A/metabolismo , Receptores Virais/metabolismo , Coronavírus Relacionado à Síndrome Respiratória Aguda Grave/química , Glicoproteína da Espícula de Coronavírus/metabolismo , Enzima de Conversão de Angiotensina 2 , Anticorpos Monoclonais/imunologia , Anticorpos Monoclonais/metabolismo , Anticorpos Antivirais/imunologia , Anticorpos Antivirais/metabolismo , Betacoronavirus/imunologia , Simulação por Computador , Mapeamento de Epitopos , Humanos , Modelos Moleculares , Método de Monte Carlo , Peptidil Dipeptidase A/química , Ligação Proteica , Conformação Proteica , Domínios e Motivos de Interação entre Proteínas , Mapeamento de Interação de Proteínas , Receptores Virais/química , Coronavírus Relacionado à Síndrome Respiratória Aguda Grave/imunologia , SARS-CoV-2 , Glicoproteína da Espícula de Coronavírus/química , Glicoproteína da Espícula de Coronavírus/imunologia , TermodinâmicaRESUMO
11-Beta hydroxysteroid dehydrogenase type 1 (11ß-HSD1) regulates cortisol levels mainly in adipose, hepatic and brain tissues. There is a relationship between the high activity of this enzyme and the development of obesity and metabolic disorders. The inhibition of 11ß-HSD1 has been shown to attenuate the development of type 2 diabetes mellitus, insulin resistance, metabolic syndrome and other diseases mediated by excessive cortisol production. In this work, fifteen benzothiazole derivatives substituted with electron-withdrawing and electron-donating groups were designed to explore their affinity for 11ß-HSD1 using in silico methods. The results show that (E)-5-((benzo[d]thiazol-2-ylimino)(methylthio)methylamino)-2-hydroxybenzoic acid (C1) has good physicochemical properties and favorable interactions with 11ß-HSD1 through hydrogen bonding and hydrophobic interactions in the catalytic site formed by Y183, S170 and Y177. Furthermore, C1 was synthesized and evaluated in vitro and ex vivo using clobenzorex (CLX) as a reference drug in obese Zucker rats. The in vitro results showed that C1 was a better inhibitor of human 11ß-HSD1 than CLX. The ex vivo assay results demonstrated that C1 was capable of reducing 11ß-HSD1 overexpression in mesenteric adipose tissue. Therefore, C1 was able to decrease the activity and expression of 11ß-HSD1 better than CLX.
Assuntos
11-beta-Hidroxiesteroide Desidrogenase Tipo 1/antagonistas & inibidores , Benzotiazóis/química , Benzotiazóis/síntese química , Inibidores Enzimáticos/química , Inibidores Enzimáticos/síntese química , Anfetaminas/farmacologia , Animais , Benzotiazóis/farmacologia , Domínio Catalítico/efeitos dos fármacos , Diabetes Mellitus Tipo 2/tratamento farmacológico , Diabetes Mellitus Tipo 2/metabolismo , Inibidores Enzimáticos/farmacologia , Humanos , Ligação de Hidrogênio/efeitos dos fármacos , Interações Hidrofóbicas e Hidrofílicas/efeitos dos fármacos , Masculino , Simulação de Acoplamento Molecular , Obesidade/tratamento farmacológico , Obesidade/metabolismo , Ratos , Ratos ZuckerRESUMO
Evaluation of ligand-binding affinity using the atomic coordinates of a protein-ligand complex is a challenge from the computational point of view. The availability of crystallographic structures of complexes with binding affinity data opens the possibility to create machine-learning models targeted to a specific protein system. Here, we describe a new methodology that combines a mass-spring system approach with supervised machine-learning techniques to predict the binding affinity of protein-ligand complexes. The combination of these techniques allows exploring the scoring function space, generating a model targeted to a protein system of interest. The new model shows superior predictive performance when compared with classical scoring functions implemented in the programs Molegro Virtual Docker, AutoDock4, and AutoDock Vina. We implemented this methodology in a new program named Taba. Taba is implemented in Python and available to download under the GNU license at https://github.com/azevedolab/taba. © 2019 Wiley Periodicals, Inc.
Assuntos
Proteínas/química , Software , Ligantes , Aprendizado de Máquina , Modelos Moleculares , TermodinâmicaRESUMO
Since the early 1980s, we have witnessed considerable progress in the development and application of docking programs to assess protein-ligand interactions. Most of these applications had as a goal the identification of potential new binders to protein targets. Another remarkable progress is taking place in the determination of the structures of protein-ligand complexes, mostly using X-ray diffraction crystallography. Considering these developments, we have a favorable scenario for the creation of a computational tool that integrates into one workflow all steps involved in molecular docking simulations. We had these goals in mind when we developed the program SAnDReS. This program allows the integration of all computational features related to modern docking studies into one workflow. SAnDReS not only carries out docking simulations but also evaluates several docking protocols allowing the selection of the best approach for a given protein system. SAnDReS is a free and open-source (GNU General Public License) computational environment for running docking simulations. Here, we describe the combination of SAnDReS and AutoDock4 for protein-ligand docking simulations. AutoDock4 is a free program that has been applied to over a thousand receptor-ligand docking simulations. The dataset described in this chapter is available for downloading at https://github.com/azevedolab/sandres.
Assuntos
Biologia Computacional/métodos , Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , Software , Sítios de Ligação , Bases de Dados Factuais , Desenho de Fármacos , Ligantes , Ligação Proteica , Proteínas/química , Interface Usuário-Computador , NavegadorRESUMO
Computational analysis of protein-ligand interactions is of pivotal importance for drug design. Assessment of ligand binding energy allows us to have a glimpse of the potential of a small organic molecule as a ligand to the binding site of a protein target. Considering scoring functions available in docking programs such as AutoDock4, AutoDock Vina, and Molegro Virtual Docker, we could say that they all rely on equations that sum each type of protein-ligand interactions to model the binding affinity. Most of the scoring functions consider electrostatic interactions involving the protein and the ligand. In this chapter, we present the main physics concepts necessary to understand electrostatics interactions relevant to molecular recognition of a ligand by the binding pocket of a protein target. Moreover, we analyze the electrostatic potential energy for an ensemble of structures to highlight the main features related to the importance of this interaction for binding affinity.
Assuntos
Ligantes , Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , Proteínas/química , Eletricidade Estática , Algoritmos , Sítios de Ligação , Desenho de Fármacos , Modelos Moleculares , Ligação ProteicaRESUMO
Van der Waals forces are determinants of the formation of protein-ligand complexes. Physical models based on the Lennard-Jones potential can estimate van der Waals interactions with considerable accuracy and with a computational complexity that allows its application to molecular docking simulations and virtual screening of large databases of small organic molecules. Several empirical scoring functions used to evaluate protein-ligand interactions approximate van der Waals interactions with the Lennard-Jones potential. In this chapter, we present the main concepts necessary to understand van der Waals interactions relevant to molecular recognition of a ligand by the binding pocket of a protein target. We describe the Lennard-Jones potential and its application to calculate potential energy for an ensemble of structures to highlight the main features related to the importance of this interaction for binding affinity.
Assuntos
Desenho de Fármacos , Modelos Teóricos , Complexos Multiproteicos/química , Proteínas/química , Algoritmos , LigantesRESUMO
Fast and reliable evaluation of the hydrogen bond potential energy has a significant impact in the drug design and development since it allows the assessment of large databases of organic molecules in virtual screening projects focused on a protein of interest. Semi-empirical force fields implemented in molecular docking programs make it possible the evaluation of protein-ligand binding affinity where the hydrogen bond potential is a common term used in the calculation. In this chapter, we describe the concepts behind the programs used to predict hydrogen bond potential energy employing semi-empirical force fields as the ones available in the programs AMBER, AutoDock4, TreeDock, and ReplicOpter. We described here the 12-10 potential and applied it to evaluate the binding affinity for an ensemble of crystallographic structures for which experimental data about binding affinity are available.
Assuntos
Ligação de Hidrogênio , Ligantes , Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , Proteínas/química , Algoritmos , Desenho de Fármacos , Ligação ProteicaRESUMO
Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic coordinates of protein-ligand complexes. These new computational tools made application of a broad spectrum of machine-learning techniques to study protein-ligand interactions possible. The essential aspect of these machine-learning approaches is to train a new computational model by using technologies such as supervised machine-learning techniques, convolutional neural network, and random forest to mention the most commonly applied methods. In this chapter, we focus on supervised machine-learning techniques and their applications in the development of protein-targeted scoring functions for the prediction of binding affinity. We discuss the development of the program SAnDReS and its application to the creation of machine-learning models to predict inhibition of cyclin-dependent kinase and HIV-1 protease. Moreover, we describe the scoring function space, and how to use it to explain the development of targeted scoring functions.
Assuntos
Aprendizado de Máquina , Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , Proteínas/química , Software , Algoritmos , Quinase 2 Dependente de Ciclina/química , Bases de Dados de Proteínas , Protease de HIV/química , Humanos , Ligantes , Modelos Estatísticos , Aprendizado de Máquina Supervisionado , NavegadorRESUMO
In the analysis of protein-ligand interactions, two abstractions have been widely employed to build a systematic approach to analyze these complexes: protein and chemical spaces. The pioneering idea of the protein space dates back to 1970, and the chemical space is newer, later 1990s. With the progress of computational methodologies to create machine-learning models to predict the ligand-binding affinity, clearly there is a need for novel approaches to the problem of protein-ligand interactions. New abstractions are required to guide the conceptual analysis of the molecular recognition problem. Using a systems approach, we proposed to address protein-ligand scoring functions using the modern idea of the scoring function space. In this chapter, we describe the fundamental concept behind the scoring function space and how it has been applied to develop the new generation of targeted-scoring functions.