Protein structure prediction

Deng, Haiyou; Jia, Ya; Zhang, Yang

doi:10.1142/s021797921840009x

Cited by 64 publications

(61 citation statements)

References 85 publications

(77 reference statements)

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“…These 3D-structures can be obtained using various experimental and computational techniques. X-ray crystallography and NMR spectroscopy are currently the two major experimental techniques for protein structure determination [8] which are deposited in both UniProt and Protein Data Bank (PDB) [9]. For computational modeling of the 3D structure of proteins, homology modeling technique is used.…”

Section: Introductionmentioning

confidence: 99%

Role of Force Fields in Protein Function Prediction

Hazarika¹,

Rajkhowa²,

Jha³

2021

Homology Molecular Modeling - Perspectives and Applications

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The world today, although, has developed an elaborate health system to fortify against known and unknown diseases, it continues to be challenged by new as well as emerging, and re-emerging infectious disease threats with severity and probable fluctuations. These threats also have varying costs for morbidity and mortality, as well as for a complex set of socioeconomic outcomes. Some of these diseases are often caused by pathogens which use humans as host. In such cases, it becomes paramount responsibility to dig out the source of pathogen survival to stop their population growth. Sequencing genomes has been finessed so much in the 21st century that complete genomes of any pathogen can be sequenced in a matter of days following which; different potential drug targets are needed to be identified. Structure modeling of the selected sequences is an initial step in structure-based drug design (SBDD). Dynamical study of predicted models provides a stable target structure. Results of these in-silico techniques greatly depend on force field (FF) parameters used. Thus, in this chapter, we intend to discuss the role of FF parameters used in protein structure prediction and molecular dynamics simulation to provide a brief overview on this area.

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Section: Introductionmentioning

confidence: 99%

Role of Force Fields in Protein Function Prediction

Hazarika¹,

Rajkhowa²,

Jha³

2021

Homology Molecular Modeling - Perspectives and Applications

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“…Proteins are one of the most interesting, widespread, and highly studied macromolecules due to their highly functional features. Proteins differ in their primary structure, which consists of a sequence of amino acids; this sequence strictly determines the spontaneous folding patterns and spatial arrangements that characterize their three-dimensional structures, which then define their diverse molecular functions 1,2 . Due to their diverse functional properties, proteins are of interest for a wide range of industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Deep learning enables the design of functionalde novoantimicrobial proteins

Cáceres-Delpiano¹,

Ibáñez²,

Alegre³

et al. 2020

Preprint

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Protein sequences are highly dimensional and present one of the main problems for the optimization and study of sequence-structure relations. The intrinsic degeneration of protein sequences is hard to follow, but the continued discovery of new protein structures has shown that there is convergence in terms of the possible folds that proteins can adopt, such that proteins with sequence identities lower than 30% may still fold into similar structures. Given that proteins share a set of conserved structural motifs, machine-learning algorithms can play an essential role in the study of sequence-structure relations. Deep-learning neural networks are becoming an important tool in the development of new techniques, such as protein modeling and design, and they continue to gain power as new algorithms are developed and as increasing amounts of data are released every day. Here, we trained a deep-learning model based on previous recurrent neural networks to design analog protein structures using representations learning based on the evolutionary and structural information of proteins. We test the capabilities of this model by creating de novo variants of an antifungal peptide, with sequence identities of 50% or lower relative to the wild-type (WT) peptide. We show by in silico approximations, such as molecular dynamics, that the new variants and the WT peptide can successfully bind to a chitin surface with comparable relative binding energies. These results are supported by in vitro assays, where the de novo designed peptides showed antifungal activity that equaled or exceeded the WT peptide.

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“…Loops are generally too short to provide information about their local fold [13]. The missing residues may be reconstructed using protein structure prediction methods, such as homology modeling [14,15] or Ab initio modeling [16]. Homology modeling is one of the template-based structure prediction methods that use an experimentally solved structure of a close homologous protein as a template to predict the structure of the target protein.…”

Section: Introductionmentioning

confidence: 99%

Simple Selection Procedure to Distinguish between Static and Flexible Loops

Mitusińska

Skalski

Góra

2020

IJMS

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Loops are the most variable and unorganized elements of the secondary structure of proteins. Their ability to shift their shape can play a role in the binding of small ligands, enzymatic catalysis, or protein–protein interactions. Due to the loop flexibility, the positions of their residues in solved structures show the largest B-factors, or in a worst-case scenario can be unknown. Based on the loops’ movements’ timeline, they can be divided into slow (static) and fast (flexible). Although most of the loops that are missing in experimental structures belong to the flexible loops group, the computational tools for loop reconstruction use a set of static loop conformations to predict the missing part of the structure and evaluate the model. We believe that these two loop types can adopt different conformations and that using scoring functions appropriate for static loops is not sufficient for flexible loops. We showed that common model evaluation methods, are insufficient in the case of flexible solvent-exposed loops. Instead, we recommend using the potential energy to evaluate such loop models. We provide a novel model selection method based on a set of geometrical parameters to distinguish between flexible and static loops without the use of molecular dynamics simulations. We have also pointed out the importance of water network and interactions with the solvent for the flexible loop modeling.

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Protein structure prediction

Cited by 64 publications

References 85 publications

Role of Force Fields in Protein Function Prediction

Role of Force Fields in Protein Function Prediction

Deep learning enables the design of functionalde novoantimicrobial proteins

Simple Selection Procedure to Distinguish between Static and Flexible Loops

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