Protein structure prediction

Agnihotry, Shikha; Pathak, Rajesh Kumar; Singh, Dev Bukhsh; Tiwari, Apoorv; Hussain, Imran

doi:10.1016/b978-0-323-89775-4.00023-7

Cited by 39 publications

(21 citation statements)

References 53 publications

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“…The prediction methods are usually divided into template-based modeling (TBM) and free modeling (FM), considering the use or not of templates (Gromiha et al, 2018;Bongirwar and Mokhade, 2022;Paiva et al, 2022), even though, recently, some TBM methods use energy-guided model refinement, and part of FM uses fragmentbased sampling approaches, extracting information from Protein Data Bank (PDB) through machine learning. Within these two groups, the algorithms developed are usually classified into three different groups: ab initio (a FM methodology), threading/fold recognition (a TBM methodology), and homology (a TBM methodology) (Gromiha et al, 2018;Agnihotry et al, 2022). Despite controversial, this classification corresponds to the categories from the Critical Assessment of Structure Prediction (CASP), a biennial competition with the aim to establish the current state of the art in protein structure prediction, which contains three categories: TBM, FM, and an intermediate category, FM/TBM.…”

Section: Structure Prediction Methodsmentioning

confidence: 99%

“…The ab initio approaches are based on the thermodynamics hypothesis that the native protein structure presents the lowest free energy possible (Hardin et al, 2002;Yuan et al, 2003;Gromiha et al, 2018;Agnihotry et al, 2022). The idea of these methods are to predict new folds considering physicochemical properties from the protein fold process, such as hydrogen bonding, contact potential energy, PDB-derived secondary structure propensities, and folding involving both bonded and non-bonded interactions.…”

Section: Structure Prediction Methodsmentioning

confidence: 99%

“…The threading/fold recognition methodologies are based on the idea that structure is more conserved than the amino acid sequence and has a limited number of protein structure folds in nature (Rost et al, 1997;Dorn et al, 2014;Gromiha et al, 2018;Agnihotry et al, 2022). They consist in choosing the best 3D template of known foldings that fit well in the target sequence considering a scoring function built on pairwise potential, second structure comparison, as well as solvent properties.…”

Section: Structure Prediction Methodsmentioning

confidence: 99%

“…The homology models derive from the fact that two amino acid sequences that are highly similar have similar structures (Dorn et al, 2014;Gromiha et al, 2018;Agnihotry et al, 2022;Sanjeevi et al, 2022). For this, the target sequence is aligned to a sequence in which the structure is known and an atomic model for the target protein is generated taking into account its similarities with the template backbone, followed by modeling loop regions and sidechains.…”

Section: Structure Prediction Methodsmentioning

confidence: 99%

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Before and after AlphaFold2: An overview of protein structure prediction

et al. 2023

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Three-dimensional protein structure is directly correlated with its function and its determination is critical to understanding biological processes and addressing human health and life science problems in general. Although new protein structures are experimentally obtained over time, there is still a large difference between the number of protein sequences placed in Uniprot and those with resolved tertiary structure. In this context, studies have emerged to predict protein structures by methods based on a template or free modeling. In the last years, different methods have been combined to overcome their individual limitations, until the emergence of AlphaFold2, which demonstrated that predicting protein structure with high accuracy at unprecedented scale is possible. Despite its current impact in the field, AlphaFold2 has limitations. Recently, new methods based on protein language models have promised to revolutionize the protein structural biology allowing the discovery of protein structure and function only from evolutionary patterns present on protein sequence. Even though these methods do not reach AlphaFold2 accuracy, they already covered some of its limitations, being able to predict with high accuracy more than 200 million proteins from metagenomic databases. In this mini-review, we provide an overview of the breakthroughs in protein structure prediction before and after AlphaFold2 emergence.

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Section: Structure Prediction Methodsmentioning

confidence: 99%

Section: Structure Prediction Methodsmentioning

confidence: 99%

Section: Structure Prediction Methodsmentioning

confidence: 99%

Section: Structure Prediction Methodsmentioning

confidence: 99%

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Before and after AlphaFold2: An overview of protein structure prediction

et al. 2023

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“…Cuando la estructura de la proteína de interés no ha sido elucidada aún en forma experimental, es posible generar predicciones o modelos de la misma mediante técnicas de modelado molecular. Existen diversas técnicas para modelar la estructura de una proteína, dentro de las cuales podemos mencionar al modelado ab initio o de primeros principios, el modelado por threading o reconocimiento de plegamiento y el modelado por homología (Agnihotry et al 2022). A continuación se detalla esta última herramienta por ser la utilizada en este trabajo de tesis.…”

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Descubrimiento de nuevos anticonvulsivos que actúan mediante interacciones con canales iónicos

Llanos¹

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Epilepsy is a disease characterized by the recurrent presence of seizures, with the neurobiological, cognitive, psychological, and social consequences that this implies. It affects more than 50 million people worldwide, which makes it the second most common neurological disease globally. The vast majority of those affected live in low-income countries and as a consequence, nearly 75% of them do not receive appropriate treatment. Pharmacotherapy is the first-line treatment for this pathology. However, approximately 30% of patients do not respond to existing pharmacological therapies. This motivates the constant search for safer and better-tolerated anticonvulsant drugs (ACDs) that overcome the drug resistance problem. In this regard, this doctoral thesis aims to find multitarget compounds that act simultaneously on TRPV1 and NaV1.2 channels, with potential anticonvulsant activity in vivo, through a computer-aided drug repositioning strategy. For both molecular targets ligand- (QSAR) and structure-based (docking and molecular dynamics) predictive models were developed. The ensemble of models was applied in a virtual screening campaign over the DrugBank database, aiming to repurpose already approved drugs as ACDs, and three candidates were selected for experimental testing: Montelukast, Novobiocin, and Cinnarizine. All of them demonstrated a potent inhibitory activity on both targets, measured by the patch clamp technique on a heterologous expression system in HEK293 cells. Additionally, the candidates were tested in four animal models of seizures: MES, 6-hz, PTZ, and GASH:Sal. All drugs exhibited anticonvulsant activity in at least one of these models, and none of them showed signs of neurotoxicity in the Rotorod test. The combination of in silico methodologies, based on the structure of the ligands and the receptor, proved to be a useful approach for the identification of multitarget compounds in the context of a disease such as Epilepsy. Moreover, the joint modulation of TRPV1 and NaV1.2 channels emerge as a promising strategy for the development of novel ACDs.

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