Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)?

Connecting the dots among the amino acid sequence of a protein, its structure, and its function remains a central theme in molecular biology, as it would have many applications in the treatment of illnesses related to misfolding or protein instability. As a result of high-throughput sequencing methods, biologists currently live in a protein sequence-rich world. However, our knowledge of protein structure based on experimental data remains comparatively limited. As a consequence, protein structure prediction has established itself as a very active field of research to fill in this gap. This field, once thought to be reserved for theoretical biophysicists, is constantly reinventing itself, borrowing ideas informed by an ever-increasing assembly of scientific domains, from biology, chemistry, (statistical) physics, mathematics, computer science, statistics, bioinformatics, and more recently data sciences. We review the recent progress arising from this integration of knowledge, from the development of specific computer architecture to allow for longer timescales in physics-based simulations of protein folding to the recent advances in predicting contacts in proteins based on detection of coevolution using very large data sets of aligned protein sequences.

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Amino acid torsion angles enable prediction of protein fold classification

Scientific Reports ◽

10.1038/s41598-020-78465-1 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Kun Tian ◽

Xin Zhao ◽

Xiaogeng Wan ◽

Stephen S.-T. Yau

Keyword(s):

Protein Structure ◽

Amino Acid ◽

Protein Structure Prediction ◽

Structure Prediction ◽

High Throughput Sequencing ◽

Protein Structures ◽

Amino Acid Sequences ◽

Single Amino Acid ◽

New Approach ◽

Protein Functions

AbstractProtein structure can provide insights that help biologists to predict and understand protein functions and interactions. However, the number of known protein structures has not kept pace with the number of protein sequences determined by high-throughput sequencing. Current techniques used to determine the structure of proteins are complex and require a lot of time to analyze the experimental results, especially for large protein molecules. The limitations of these methods have motivated us to create a new approach for protein structure prediction. Here we describe a new approach to predict of protein structures and structure classes from amino acid sequences. Our prediction model performs well in comparison with previous methods when applied to the structural classification of two CATH datasets with more than 5000 protein domains. The average accuracy is 92.5% for structure classification, which is higher than that of previous research. We also used our model to predict four known protein structures with a single amino acid sequence, while many other existing methods could only obtain one possible structure for a given sequence. The results show that our method provides a new effective and reliable tool for protein structure prediction research.

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

Plant Tissue Culture and Biotechnology ◽

10.3329/ptcb.v19i2.5439 ◽

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)

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