scholarly journals Assessment of AI-based Protein Structure Prediction for the NLRP3 Target

2021 ◽  
Author(s):  
Jian Yin ◽  
Jialin Yu ◽  
Weiren Cui ◽  
Junkun Lei ◽  
Wenhua Chen ◽  
...  
Keyword(s):  
Protein Structure ◽  
Drug Discovery ◽  
Small Molecule ◽  
Structure Prediction ◽  
3D Model ◽  
Protein Structures ◽  
Binding Modes ◽  
Careful Evaluation ◽  
Small Molecule Drug ◽  
Recent Success

The recent success of AlphaFold and RoseTTAFold has demonstrated the values of AI methods in predicting highly accurate protein structures. Despite the advances, their roles in the context of small-molecule drug discovery need to be thoroughly explored. In this study, we evaluated the possibility whether the AI-based models can lead to reliable 3D structures of protein-ligand complexes. With the AI-generated protein structure, we were able to confidently predict the binding modes of small-molecule inhibitors for NLRP3, a challenging protein target in terms of obtaining the 3D model both experimentally and computationally. We therefore concluded that through careful evaluation, AI-predicted protein structures can be trusted and useful for small-molecule drug discovery projects.

scholarly journals Protein structure search to support the development of protein structure prediction methods

2020 ◽  
Author(s):  
Ronald Ayoub ◽  
Yugyung Lee
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Unsolved Problem ◽  
Protein Structures ◽  
Prediction Methods ◽  
Link Type ◽  
Recent Success ◽  
Structure Search ◽  
Structure Similarity

AbstractProtein structure prediction is a long-standing unsolved problem in molecular biology that has seen renewed interest with the recent success of deep learning with AlphaFold at CASP13. While developing and evaluating protein structure prediction methods, researchers may want to identify the most similar known structures to their predicted structures. These predicted structures often have low sequence and structure similarity to known structures. We show how RUPEE, a purely geometric protein structure search, is able to identify the structures most similar to structure predictions, regardless of how they vary from known structures, something existing protein structure searches struggle with. RUPEE accomplishes this through the use of a novel linear encoding of protein structures as a sequence of residue descriptors. Using a fast Needleman-Wunsch algorithm, RUPEE is able to perform alignments on the sequences of residue descriptors for every available structure. This is followed by a series of increasingly accurate structure alignments from TM-align alignments initialized with the Needleman-Wunsch residue descriptor alignments to standard TM-align alignments of the final results. By using alignment normalization effectively at each stage, RUPEE also can execute containment searches in addition to full-length searches to identify structural motifs within proteins. We compare the results of RUPEE to mTM-align, SSM, CATHEDRAL and VAST using a benchmark derived from the protein structure predictions submitted to CASP13. RUPEE identifies better alignments on average with respect to RMSD and TM-score as well as Q-score and SSAP-score, scores specific to SSM and CATHEDRAL, respectively. Finally, we show a sample of the top-scoring alignments that RUPEE identified that none of the other protein structure searches we compared to were able to identify.The RUPEE protein structure search is available at https://ayoubresearch.com. Code and data are available at https://github.com/rayoub/rupee.

Sequence Specific Dihedral Angle Distribution: Application in Protein Structure Prediction and Evaluation

1970 ◽  
Vol 19 (2) ◽  
pp. 217-226
Author(s):  
S. M. Minhaz Ud-Dean ◽  
Mahdi Muhammad Moosa
Keyword(s):  
Protein Structure ◽  
Dihedral Angle ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Structures ◽  
Angle Distribution ◽  
Ramachandran Plot ◽  
Specific Data ◽  
Specific Distribution ◽  
Structure Evaluation

Protein structure prediction and evaluation is one of the major fields of computational biology. Estimation of dihedral angle can provide information about the acceptability of both theoretically predicted and experimentally determined structures. Here we report on the sequence specific dihedral angle distribution of high resolution protein structures available in PDB and have developed Sasichandran, a tool for sequence specific dihedral angle prediction and structure evaluation. This tool will allow evaluation of a protein structure in pdb format from the sequence specific distribution of Ramachandran angles. Additionally, it will allow retrieval of the most probable Ramachandran angles for a given sequence along with the sequence specific data. Key words: Torsion angle, φ-ψ distribution, sequence specific ramachandran plot, Ramasekharan, protein structure appraisal D.O.I. 10.3329/ptcb.v19i2.5439 Plant Tissue Cult. & Biotech. 19(2): 217-226, 2009 (December)

Targeted Protein Degradation: "The Gold Rush is On!"

2020 ◽  
Vol 7 (1) ◽  
pp. 4-16
Author(s):  
Daria Kotlarek ◽  
Agata Pawlik ◽  
Maria Sagan ◽  
Marta Sowała ◽  
Alina Zawiślak-Architek ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Degradation ◽  
Small Molecule ◽  
Gold Rush ◽  
European Company ◽  
Drug Candidates ◽  
Protein Inhibition ◽  
Small Molecule Drug ◽  
Therapeutic Benefits ◽  
Talent Pool

Targeted Protein Degradation (TPD) is an emerging new modality of drug discovery that offers unprecedented therapeutic benefits over traditional protein inhibition. Most importantly, TPD unlocks the untapped pool of the proteome that to date has been considered undruggable. Captor Therapeutics (Captor) is the fourth global, and first European, company that develops small molecule drug candidates based on the principles of targeted protein degradation. Captor is located in Basel, Switzerland and Wroclaw, Poland and exploits the best opportunities of the two sites – experience and non-dilutive European grants, and talent pool, respectively. Through over $38 M of funding, Captor has been active in three areas of TPD: molecular glues, bi-specific degraders and direct degraders, ObteronsTM.

scholarly journals Glossary and tutorial of xenobiotic metabolism terms used during small molecule drug discovery and development (IUPAC Technical Report)

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Paul Erhardt ◽  
Kenneth Bachmann ◽  
Donald Birkett ◽  
Michael Boberg ◽  
Nicholas Bodor ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecule ◽  
Chemical Structure ◽  
Early Stage ◽  
Xenobiotic Metabolism ◽  
Drug Discovery And Development ◽  
Small Molecule Drug ◽  
Technical Report ◽  
Wide Range ◽  
Draft Version

Abstract This project originated more than 15 years ago with the intent to produce a glossary of drug metabolism terms having definitions especially applicable for use by practicing medicinal chemists. A first-draft version underwent extensive beta-testing that, fortuitously, engaged international audiences in a wide range of disciplines involved in drug discovery and development. It became clear that the inclusion of information to enhance discussions among this mix of participants would be even more valuable. The present version retains a chemical structure theme while expanding tutorial comments that aim to bridge the various perspectives that may arise during interdisciplinary communications about a given term. This glossary is intended to be educational for early stage researchers, as well as useful for investigators at various levels who participate on today’s highly multidisciplinary, collaborative small molecule drug discovery teams.

Prediction of Structural and Functional Aspects of Protein

2014 ◽  
pp. 317-333
Author(s):  
Arun G. Ingale
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Sequence Information ◽  
Predict Protein Structure ◽  
Basic Ideas

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.

scholarly journals Protein Structure Determination in Living Cells

2019 ◽  
Vol 20 (10) ◽  
pp. 2442 ◽  
Author(s):  
Teppei Ikeya ◽  
Peter Güntert ◽  
Yutaka Ito
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
Structure Prediction ◽  
Structural Information ◽  
Nuclear Overhauser Effect ◽  
Protein Structures ◽  
Three Dimensional ◽  
Structural Data ◽  
Sample Tube ◽  
In Cells

To date, in-cell NMR has elucidated various aspects of protein behaviour by associating structures in physiological conditions. Meanwhile, current studies of this method mostly have deduced protein states in cells exclusively based on ‘indirect’ structural information from peak patterns and chemical shift changes but not ‘direct’ data explicitly including interatomic distances and angles. To fully understand the functions and physical properties of proteins inside cells, it is indispensable to obtain explicit structural data or determine three-dimensional (3D) structures of proteins in cells. Whilst the short lifetime of cells in a sample tube, low sample concentrations, and massive background signals make it difficult to observe NMR signals from proteins inside cells, several methodological advances help to overcome the problems. Paramagnetic effects have an outstanding potential for in-cell structural analysis. The combination of a limited amount of experimental in-cell data with software for ab initio protein structure prediction opens an avenue to visualise 3D protein structures inside cells. Conventional nuclear Overhauser effect spectroscopy (NOESY)-based structure determination is advantageous to elucidate the conformations of side-chain atoms of proteins as well as global structures. In this article, we review current progress for the structure analysis of proteins in living systems and discuss the feasibility of its future works.

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