Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 169
Filtrar
1.
Nat Commun ; 13(1): 1744, 2022 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-35365655

RESUMO

Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system.


Assuntos
Aprendizado Profundo , Escherichia coli/genética , Redes Neurais de Computação , Proteoma , Alinhamento de Sequência
2.
Cancer Inform ; 20: 11769351211065979, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34924752

RESUMO

BACKGROUND: Colorectal cancer is the third largest cause of cancer-related mortality worldwide. Although current treatments with chemotherapeutics have allowed for management of colorectal cancer, additional novel treatments are essential. Intervening with the metabolic reprogramming observed in cancers called "Warburg Effect," is one of the novel strategies considered to combat cancers. In the metabolic reprogramming pathway, pyruvate dehydrogenase kinase (PDK1) plays a pivotal role. Identification and characterization of a PDK1 inhibitor is of paramount importance. Further, for efficacious treatment of colorectal cancers, combinatorial regimens are essential. To this end, we opted to identify a PDK1 inhibitor using computational structure-based drug design FINDSITEcomb and perform combinatorial studies with 5-FU for efficacious treatment of colorectal cancers. METHODS: Using computational structure-based drug design FINDSITEcomb, stearic acid (SA) was identified as a possible PDK1 inhibitor. Elucidation of the mechanism of action of SA was performed using flow cytometry, clonogenic assays. RESULTS: When the growth inhibitory potential of SA was tested on colorectal adenocarcinoma (DLD-1) cells, a 50% inhibitory concentration (IC50) of 60 µM was recorded. Moreover, SA inhibited the proliferation potential of DLD-1 cells as shown by the clonogenic assay and there was a sustained response even after withdrawal of the compound. Elucidation of the mechanism of action revealed, that the inhibitory effect of SA was through the programmed cell death pathway. There was increase in the number of apoptotic and multicaspase positive cells. SA also impacted the levels of the cell survival protein Bcl-2. With the aim of achieving improved treatment for colorectal cancer, we opted to combine 5-fluorouracil (5-FU), the currently used drug in the clinic, with SA. Combining SA with 5-FU, revealed a synergistic effect in which the IC50 of 5-FU decreased from 25 to 6 µM upon combination with 60 µM SA. Further, SA did not inhibit non-tumorigenic NIH-3T3 proliferation. CONCLUSIONS: We envision that this significant decrease in the IC50 of 5-FU could translate into less side effects of 5-FU and increase the efficacy of the treatment due to the multifaceted action of SA. The data generated from the current studies on the inhibition of colorectal adenocarcinoma by SA discovered by the use of the computational program as well as synergistic action with 5-FU should open up novel therapeutic options for the management of colorectal adenocarcinomas.

3.
Sci Rep ; 11(1): 20864, 2021 10 21.
Artigo em Inglês | MEDLINE | ID: mdl-34675303

RESUMO

Following SARS-CoV-2 infection, some COVID-19 patients experience severe host driven adverse events. To treat these complications, their underlying etiology and drug treatments must be identified. Thus, a novel AI methodology MOATAI-VIR, which predicts disease-protein-pathway relationships and repurposed FDA-approved drugs to treat COVID-19's clinical manifestations was developed. SARS-CoV-2 interacting human proteins and GWAS identified respiratory failure genes provide the input from which the mode-of-action (MOA) proteins/pathways of the resulting disease comorbidities are predicted. These comorbidities are then mapped to their clinical manifestations. To assess each manifestation's molecular basis, their prioritized shared proteins were subject to global pathway analysis. Next, the molecular features associated with hallmark COVID-19 phenotypes, e.g. unusual neurological symptoms, cytokine storms, and blood clots were explored. In practice, 24/26 of the major clinical manifestations are successfully predicted. Three major uncharacterized manifestation categories including neoplasms are also found. The prevalence of neoplasms suggests that SARS-CoV-2 might be an oncovirus due to shared molecular mechanisms between oncogenesis and viral replication. Then, repurposed FDA-approved drugs that might treat COVID-19's clinical manifestations are predicted by virtual ligand screening of the most frequent comorbid protein targets. These drugs might help treat both COVID-19's severe adverse events and lesser ones such as loss of taste/smell.


Assuntos
COVID-19/complicações , COVID-19/diagnóstico , COVID-19/tratamento farmacológico , Biologia Computacional/métodos , Neoplasias/complicações , Doenças do Sistema Nervoso/complicações , Trombose/complicações , Replicação Viral , Benchmarking , Comorbidade , Simulação por Computador , Síndrome da Liberação de Citocina , Descoberta de Drogas , Humanos , Aprendizado de Máquina , Medicina Molecular , Fenótipo , SARS-CoV-2 , Resultado do Tratamento
4.
J Chem Inf Model ; 61(10): 4827-4831, 2021 10 25.
Artigo em Inglês | MEDLINE | ID: mdl-34586808

RESUMO

AlphaFold 2 (AF2) was the star of CASP14, the last biannual structure prediction experiment. Using novel deep learning, AF2 predicted the structures of many difficult protein targets at or near experimental resolution. Here, we present our perspective of why AF2 works and show that it is a very sophisticated fold recognition algorithm that exploits the completeness of the library of single domain PDB structures. It has also learned local side chain packing rearrangements that enable it to refine proteins to high resolution. The benefits and limitations of its ability to predict the structures of many more proteins at or close to atomic detail are discussed.


Assuntos
Dobramento de Proteína , Proteínas , Algoritmos , Sequência de Aminoácidos
5.
Biochem (Lond) ; 43(1): 4-12, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-34219990

RESUMO

Many of life's molecules including proteins are built from chiral building blocks. What drove homochiral building block selection? Simulations on demi-chiral proteins containing equal numbers of d- and l-amino acids show that they possess many modern homochiral protein properties. They have the same global folds and could do the same biochemistry, with ancient, essential functions being most prevalent. They could synthesize chiral RNA and lipids which formed vesicles. RNA eventually combined with proteins creating ribosomes for more efficient protein synthesis, and thus, life began. Increased native state stability from homochiral secondary structure hydrogen bonding helped drive proteins towards homochirality.

6.
Front Bioinform ; 12021 May.
Artigo em Inglês | MEDLINE | ID: mdl-34308415

RESUMO

During the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural alignments. SAdLSA shows significant improvement over established sequence alignment methods. In this contribution, we show that SAdLSA provides a general machine-learning framework for structurally characterizing protein sequences. By aligning a protein sequence against itself, SAdLSA generates a fold distogram for the input sequence, including challenging cases whose structural folds were not present in the training set. About 70% of the predicted distograms are statistically significant. Although at present the accuracy of the intra-sequence distogram predicted by SAdLSA self-alignment is not as good as deep-learning algorithms specifically trained for distogram prediction, it is remarkable that the prediction of single protein structures is encoded by an algorithm that learns ensembles of pairwise structural comparisons, without being explicitly trained to recognize individual structural folds. As such, SAdLSA can not only predict protein folds for individual sequences, but also detects subtle, yet significant, structural relationships between multiple protein sequences using the same deep-learning neural network. The former reduces to a special case in this general framework for protein sequence annotation.

7.
J Chem Theory Comput ; 17(4): 2011-2012, 2021 04 13.
Artigo em Inglês | MEDLINE | ID: mdl-33730492
8.
J Chem Inf Model ; 61(4): 2074-2089, 2021 04 26.
Artigo em Inglês | MEDLINE | ID: mdl-33724022

RESUMO

To reduce time and cost, virtual ligand screening (VLS) often precedes experimental ligand screening in modern drug discovery. Traditionally, high-resolution structure-based docking approaches rely on experimental structures, while ligand-based approaches need known binders to the target protein and only explore their nearby chemical space. In contrast, our structure-based FINDSITEcomb2.0 approach takes advantage of predicted, low-resolution structures and information from ligands that bind distantly related proteins whose binding sites are similar to the target protein. Using a boosted tree regression machine learning framework, we significantly improved FINDSITEcomb2.0 by integrating ligand fragment scores as encoded by molecular fingerprints with the global ligand similarity scores of FINDSITEcomb2.0. The new approach, FRAGSITE, exploits our observation that ligand fragments, e.g., rings, tend to interact with stereochemically conserved protein subpockets that also occur in evolutionarily unrelated proteins. FRAGSITE was benchmarked on the 102 protein DUD-E set, where any template protein whose sequence identify >30% to the target was excluded. Within the top 100 ranked molecules, FRAGSITE improves VLS precision and recall by 14.3 and 18.5%, respectively, relative to FINDSITEcomb2.0. Moreover, the mean top 1% enrichment factor increases from 25.2 to 30.2. On average, both outperform state-of-the-art deep learning-based methods such as AtomNet. On the more challenging unbiased set LIT-PCBA, FRAGSITE also shows better performance than ligand similarity-based and docking approaches such as two-dimensional ECFP4 and Surflex-Dock v.3066. On a subset of 23 targets from DEKOIS 2.0, FRAGSITE shows much better performance than the boosted tree regression-based, vScreenML scoring function. Experimental testing of FRAGSITE's predictions shows that it has more hits and covers a more diverse region of chemical space than FINDSITEcomb2.0. For the two proteins that were experimentally tested, DHFR, a well-studied protein that catalyzes the conversion of dihydrofolate to tetrahydrofolate, and the kinase ACVR1, FRAGSITE identified new small-molecule nanomolar binders. Interestingly, one new binder of DHFR is a kinase inhibitor predicted to bind in a new subpocket. For ACVR1, FRAGSITE identified new molecules that have diverse scaffolds and estimated nanomolar to micromolar affinities. Thus, FRAGSITE shows significant improvement over prior state-of-the-art ligand virtual screening approaches. A web server is freely available for academic users at http:/sites.gatech.edu/cssb/FRAGSITE.


Assuntos
Descoberta de Drogas , Proteínas , Sítios de Ligação , Ligantes , Simulação de Acoplamento Molecular , Ligação Proteica , Conformação Proteica , Proteínas/metabolismo
9.
Bioinformatics ; 37(4): 490-496, 2021 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-32960943

RESUMO

MOTIVATION: From evolutionary interference, function annotation to structural prediction, protein sequence comparison has provided crucial biological insights. While many sequence alignment algorithms have been developed, existing approaches often cannot detect hidden structural relationships in the 'twilight zone' of low sequence identity. To address this critical problem, we introduce a computational algorithm that performs protein Sequence Alignments from deep-Learning of Structural Alignments (SAdLSA, silent 'd'). The key idea is to implicitly learn the protein folding code from many thousands of structural alignments using experimentally determined protein structures. RESULTS: To demonstrate that the folding code was learned, we first show that SAdLSA trained on pure α-helical proteins successfully recognizes pairs of structurally related pure ß-sheet protein domains. Subsequent training and benchmarking on larger, highly challenging datasets show significant improvement over established approaches. For challenging cases, SAdLSA is ∼150% better than HHsearch for generating pairwise alignments and ∼50% better for identifying the proteins with the best alignments in a sequence library. The time complexity of SAdLSA is O(N) thanks to GPU acceleration. AVAILABILITY AND IMPLEMENTATION: Datasets and source codes of SAdLSA are available free of charge for academic users at http://sites.gatech.edu/cssb/sadlsa/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Aprendizado Profundo , Algoritmos , Dobramento de Proteína , Alinhamento de Sequência , Software
10.
Curr Opin Struct Biol ; 68: 1-8, 2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-33129066

RESUMO

The tertiary structure of a native protein is dictated by the interplay of local secondary structure propensities, hydrogen bonding, and tertiary interactions. It is argued that the space of known protein topologies covers all single domain folds and results from the compactness of the native structure and excluded volume. Protein compactness combined with the chirality of the protein's side chains also yields native-like Ramachandran plots. It is the many-body, tertiary interactions among residues that collectively select for the global structure that a particular protein sequence adopts. This explains why the recent advances in deep-learning approaches that predict protein side-chain contacts, the distance matrix between residues, and sequence alignments are successful. They succeed because they implicitly learned the many-body interactions among protein residues.


Assuntos
Dobramento de Proteína , Proteínas , Modelos Moleculares , Estrutura Secundária de Proteína , Estrutura Terciária de Proteína , Alinhamento de Sequência
11.
Workshop Mach Learn HPC Environ ; 2021: 46-57, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-35112110

RESUMO

Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC practices. In this work, we present a novel HPC pipeline that incorporates various machine-learning approaches for structure-based functional annotation of proteins on the scale of whole genomes. Our pipeline makes extensive use of deep learning and provides computational insights into best practices for training advanced deep-learning models for high-throughput data such as proteomics data. We showcase methodologies our pipeline currently supports and detail future tasks for our pipeline to envelop, including large-scale sequence comparison using SAdLSA and prediction of protein tertiary structures using AlphaFold2.

12.
J Phys Chem B ; 124(46): 10301-10302, 2020 11 19.
Artigo em Inglês | MEDLINE | ID: mdl-33095009
14.
Mol Pharm ; 17(5): 1558-1574, 2020 05 04.
Artigo em Inglês | MEDLINE | ID: mdl-32237745

RESUMO

To improve the drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the drug's chemical structure is needed. In contrast, extant predictive methods do not comprehensively address these aspects of drug discovery and rely on features derived from extensive, often unavailable experimental information for novel molecules. To address these issues, we developed MEDICASCY, a multilabel-based boosted random forest machine learning method that only requires the small molecule's chemical structure for the drug side effect, indication, efficacy, and probable mode of action target predictions; however, it has comparable or even significantly better performance than existing approaches requiring far more information. In retrospective benchmarking on high confidence predictions, MEDICASCY shows about 78% precision and recall for predicting at least one severe side effect and 72% precision drug efficacy. Experimental validation of MEDICASCY's efficacy predictions on novel molecules shows close to 80% precision for the inhibition of growth in ovarian, breast, and prostate cancer cell lines. Thus, MEDICASCY should improve the success rate for new drug approval. A web service for academic users is available at http://pwp.gatech.edu/cssb/MEDICASCY.


Assuntos
Descoberta de Drogas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Aprendizado de Máquina , Benchmarking , Linhagem Celular Tumoral , Humanos , Estudos Retrospectivos
15.
Sci Rep ; 10(1): 6140, 2020 04 09.
Artigo em Inglês | MEDLINE | ID: mdl-32273545

RESUMO

Diffuse intrinsic pontine glioma (DIPG) is a lethal pediatric brain cancer whose median survival time is under one year. The possible roles of the two most common DIPG associated cytoplasmic ACVR1 receptor kinase domain mutants, G328V and R206H, are reexamined in the context of new biochemical results regarding their intrinsic relative ATPase activities. At 37 °C, the G328V mutant displays a 1.8-fold increase in intrinsic kinase activity over wild-type, whereas the R206H mutant shows similar activity. The higher G328V mutant intrinsic kinase activity is consistent with the statistically significant longer overall survival times of DIPG patients harboring ACVR1 G328V tumors. Based on the potential cross-talk between ACVR1 and TßRI pathways and known and predicted off-targets of ACVR1 inhibitors, we further validated the inhibition effects of several TßRI inhibitors on ACVR1 wild-type and G328V mutant patient tumor derived DIPG cell lines at 20-50 µM doses. SU-DIPG-IV cells harboring the histone H3.1K27M and activating ACVR1 G328V mutations appeared to be less susceptible to TßRI inhibition than SF8628 cells harboring the H3.3K27M mutation and wild-type ACVR1. Thus, inhibition of hidden oncogenic signaling pathways in DIPG such as TßRI that are not limited to ACVR1 itself may provide alternative entry points for DIPG therapeutics.


Assuntos
Receptores de Ativinas Tipo I/genética , Neoplasias do Tronco Encefálico/genética , Glioma Pontino Intrínseco Difuso/genética , Mutação/genética , Receptores de Fatores de Crescimento Transformadores beta/metabolismo , Benzazepinas/farmacologia , Neoplasias do Tronco Encefálico/tratamento farmacológico , Neoplasias do Tronco Encefálico/enzimologia , Neoplasias do Tronco Encefálico/mortalidade , Linhagem Celular Tumoral , Glioma Pontino Intrínseco Difuso/tratamento farmacológico , Glioma Pontino Intrínseco Difuso/enzimologia , Glioma Pontino Intrínseco Difuso/mortalidade , Relação Dose-Resposta a Droga , Humanos , Imidazóis/farmacologia , Panobinostat/farmacologia , Fosfotransferases/metabolismo , Prognóstico , Conformação Proteica , Pirimidinas/farmacologia , Quinoxalinas/farmacologia , Receptor Cross-Talk , Receptores de Fatores de Crescimento Transformadores beta/antagonistas & inibidores
16.
Mar Drugs ; 18(3)2020 03 18.
Artigo em Inglês | MEDLINE | ID: mdl-32197482

RESUMO

A new cyclic peptide, kakeromamide B (1), and previously described cytotoxic cyanobacterial natural products ulongamide A (2), lyngbyabellin A (3), 18E-lyngbyaloside C (4), and lyngbyaloside (5) were identified from an antimalarial extract of the Fijian marine cyanobacterium Moorea producens. Compounds 1 and 1 exhibited moderate activity against Plasmodium falciparum blood-stages with EC50 values of 0.89 and 0.99 µM, respectively, whereas 3 was more potent with an EC50 value of 0.15 nM, respectively. Compounds 1, 4, and 5 displayed moderate liver-stage antimalarial activity against P. berghei liver schizonts with EC50 values of 1.1, 0.71, and 0.45 µM, respectively. The threading-based computational method FINDSITEcomb2.0 predicted the binding of 1 and 2 to potentially druggable proteins of Plasmodium falciparum, prompting formulation of hypotheses about possible mechanisms of action. Kakeromamide B (1) was predicted to bind to several Plasmodium actin-like proteins and a sortilin protein suggesting possible interference with parasite invasion of host cells. When 1 was tested in a mammalian actin polymerization assay, it stimulated actin polymerization in a dose-dependent manner, suggesting that 1 does, in fact, interact with actin.


Assuntos
Antimaláricos/farmacologia , Cianobactérias , Peptídeos Cíclicos/farmacologia , Policetídeos/farmacologia , Antimaláricos/química , Produtos Biológicos , Fiji , Humanos , Oceanos e Mares , Peptídeos Cíclicos/química , Plasmodium falciparum/efeitos dos fármacos , Policetídeos/química
17.
Artigo em Inglês | MEDLINE | ID: mdl-31822617

RESUMO

Living systems have chiral molecules, e.g., native proteins that almost entirely contain L-amino acids. How protein homochirality emerged from a background of equal numbers of L and D amino acids is among many questions about life's origin. The origin of homochirality and its implications are explored in computer simulations examining the stability and structural and functional properties of an artificial library of compact proteins containing 1:1 (termed demi-chiral), 3:1, and 1:3 ratios of D:L and purely L or D amino acids generated without functional selection. Demi-chiral proteins have shorter secondary structures and fewer internal hydrogen bonds and are less stable than homochiral proteins. Selection for hydrogen bonding yields a preponderance of L or D amino acids. Demi-chiral proteins have native global folds, including similarity to early ribosomal proteins, similar small molecule ligand binding pocket geometries, and many constellations of L-chiral amino acids with a 1.0-Å RMSD to native enzyme active sites. For a representative subset containing 550 active site geometries matching 457 (2) 4-digit (3-digit) enzyme classification (E.C.) numbers, native active site amino acids were generated at random for 472 of 550 cases. This increases to 548 of 550 cases when similar residues are allowed. The most frequently generated sequences correspond to ancient enzymatic functions, e.g., glycolysis, replication, and nucleotide biosynthesis. Surprisingly, even without selection, demi-chiral proteins possess the requisite marginal biochemical function and structure of modern proteins, but were thermodynamically less stable. If demi-chiral proteins were present, they could engage in early metabolism, which created the feedback loop for transcription and cell formation.

18.
Sci Rep ; 9(1): 3514, 2019 03 05.
Artigo em Inglês | MEDLINE | ID: mdl-30837676

RESUMO

The amino acid sequence of a protein encodes the blueprint of its native structure. To predict the corresponding structural fold from the protein's sequence is one of most challenging problems in computational biology. In this work, we introduce DESTINI (deep structural inference for proteins), a novel computational approach that combines a deep-learning algorithm for protein residue/residue contact prediction with template-based structural modelling. For the first time, the significantly improved predictive ability is demonstrated in the large-scale tertiary structure prediction of over 1,200 single-domain proteins. DESTINI successfully predicts the tertiary structure of four times the number of "hard" targets (those with poor quality templates) that were previously intractable, viz, a "glass-ceiling" for previous template-based approaches, and also improves model quality for "easy" targets (those with good quality templates). The significantly better performance by DESTINI is largely due to the incorporation of better contact prediction into template modelling. To understand why deep-learning accomplishes more accurate contact prediction, systematic clustering reveals that deep-learning predicts coherent, native-like contact patterns compared to co-evolutionary analysis. Taken together, this work presents a promising strategy towards solving the protein structure prediction problem.


Assuntos
Biologia Computacional/métodos , Aprendizado Profundo , Proteínas/química , Estrutura Terciária de Proteína
19.
Struct Dyn ; 6(2): 024701, 2019 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-30868089

RESUMO

Time-resolved crystallography is a powerful technique to elucidate molecular mechanisms at both spatial (angstroms) and temporal (picoseconds to seconds) resolutions. We recently discovered an unusually slow reaction at room temperature that occurs on the order of days: the in crystalline reverse oxidative decay of the chemically labile (6S)-5,6,7,8-tetrahydrofolate in complex with its producing enzyme Escherichia coli dihydrofolate reductase. Here, we report the critical analysis of a representative dataset at an intermediate reaction time point. A quinonoid-like intermediate state lying between tetrahydrofolate and dihydrofolate features a near coplanar geometry of the bicyclic pterin moiety, and a tetrahedral sp 3 C6 geometry is proposed based on the apparent mFo-DFc omit electron densities of the ligand. The presence of this intermediate is strongly supported by Bayesian difference refinement. Isomorphous Fo-Fo difference map and multi-state refinement analyses suggest the presence of end-state ligand populations as well, although the putative intermediate state is likely the most populated. A similar quinonoid intermediate previously proposed to transiently exist during the oxidation of tetrahydrofolate was confirmed by polarography and UV-vis spectroscopy to be relatively stable in the oxidation of its close analog tetrahydropterin. We postulate that the constraints on the ligand imposed by the interactions with the protein environment might be the origin of the slow reaction observed by time-resolved crystallography.

20.
Med Res Rev ; 39(2): 684-705, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30192413

RESUMO

Escherichia coli Dihydrofolate reductase is an important enzyme that is essential for the survival of the Gram-negative microorganism. Inhibitors designed against this enzyme have demonstrated application as antibiotics. However, either because of poor bioavailability of the small-molecules resulting from their inability to cross the double membrane in Gram-negative bacteria or because the microorganism develops resistance to the antibiotics by mutating the DHFR target, discovery of new antibiotics against the enzyme is mandatory to overcome drug-resistance. This review summarizes the field of DHFR inhibition with special focus on recent efforts to effectively interface computational and experimental efforts to discover novel classes of inhibitors that target allosteric and active-sites in drug-resistant variants of EcDHFR.


Assuntos
Antibacterianos/farmacologia , Infecções Bacterianas/tratamento farmacológico , Inibidores Enzimáticos/farmacologia , Escherichia coli/enzimologia , Antagonistas do Ácido Fólico/farmacologia , Tetra-Hidrofolato Desidrogenase/química , Algoritmos , Sítio Alostérico , Animais , Domínio Catalítico , Desenho de Fármacos , Descoberta de Drogas , Humanos , Ligantes , Permeabilidade/efeitos dos fármacos , Relação Estrutura-Atividade
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...