Using the Rosetta algorithm and selected inter-residue distances to predict protein structure

2008 ◽  
Vol 108 (15) ◽  
pp. 2793-2802 ◽  
Author(s):  
Christina Crecca ◽  
Adrian E. Roitberg
Keyword(s):  
Protein Structure ◽  
Predict Protein Structure

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.

Using PconsC4 and PconsFold2 to Predict Protein Structure

2019 ◽  
Vol 66 (1) ◽  
Author(s):  
Claudio Bassot ◽  
David Menendez Hurtado ◽  
Arne Elofsson
Keyword(s):  
Protein Structure ◽  
Predict Protein Structure

Combining MONSSTER and LES/PME to Predict Protein Structure from Amino Acid Sequence:  Application to the Small Protein CMTI-1

2000 ◽  
Vol 122 (35) ◽  
pp. 8392-8402 ◽  
Author(s):  
Carlos Simmerling ◽  
Matthew R. Lee ◽  
Angel. R. Ortiz ◽  
Andrzej Kolinski ◽  
Jeffrey Skolnick ◽  
...  
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Small Protein ◽  
Predict Protein Structure

scholarly journals Accurate protein structure prediction with hydroxyl radical protein footprinting data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sarah E. Biehn ◽  
Steffen Lindert
Keyword(s):  
Protein Structure ◽  
Hydroxyl Radical ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Solvent Exposure ◽  
Protein Footprinting ◽  
Hydroxyl Radical Protein Footprinting ◽  
Predict Protein Structure ◽  
Ab Initio Models

AbstractHydroxyl radical protein footprinting (HRPF) in combination with mass spectrometry reveals the relative solvent exposure of labeled residues within a protein, thereby providing insight into protein tertiary structure. HRPF labels nineteen residues with varying degrees of reliability and reactivity. Here, we are presenting a dynamics-driven HRPF-guided algorithm for protein structure prediction. In a benchmark test of our algorithm, usage of the dynamics data in a score term resulted in notable improvement of the root-mean-square deviations of the lowest-scoring ab initio models and improved the funnel-like metric Pnear for all benchmark proteins. We identified models with accurate atomic detail for three of the four benchmark proteins. This work suggests that HRPF data along with side chain dynamics sampled by a Rosetta mover ensemble can be used to accurately predict protein structure.

Using Machine Learning to Predict Protein Structure from Spectral Data

2010 ◽  
Author(s):  
Myra Kinalwa ◽  
Andrew J. Doig ◽  
Ewan W. Blanch ◽  
P. M. Champion ◽  
L. D. Ziegler
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Spectral Data ◽  
Predict Protein Structure

scholarly journals Selection for cooperativity causes epistasis predominately between native contacts and enables epistasis-based structure reconstruction

2021 ◽  
Vol 118 (16) ◽  
pp. e2010057118
Author(s):  
R. Charlotte Eccleston ◽  
David D. Pollock ◽  
Richard A. Goldstein
Keyword(s):  
Protein Structure ◽  
Native Structure ◽  
Guanine Nucleotide Binding Protein ◽  
Epistatic Interactions ◽  
Intermediate States ◽  
Predict Protein Structure ◽  
Selection For ◽  
Guanine Nucleotide Binding ◽  
Energetic Interactions ◽  
Protein Gb1

Epistasis and cooperativity of folding both result from networks of energetic interactions in proteins. Epistasis results from energetic interactions among mutants, whereas cooperativity results from energetic interactions during folding that reduce the presence of intermediate states. The two concepts seem intuitively related, but it is unknown how they are related, particularly in terms of selection. To investigate their relationship, we simulated protein evolution under selection for cooperativity and separately under selection for epistasis. Strong selection for cooperativity created strong epistasis between contacts in the native structure but weakened epistasis between nonnative contacts. In contrast, selection for epistasis increased epistasis in both native and nonnative contacts and reduced cooperativity. Because epistasis can be used to predict protein structure only if it preferentially occurs in native contacts, this result indicates that selection for cooperativity may be key for predicting structure using epistasis. To evaluate this inference, we simulated the evolution of guanine nucleotide-binding protein (GB1) with and without cooperativity. With cooperativity, strong epistatic interactions clearly map out the native GB1 structure, while allowing the presence of intermediate states (low cooperativity) obscured the structure. This indicates that using epistasis measurements to reconstruct protein structure may be inappropriate for proteins with stable intermediates.

scholarly journals Single-sequence protein structure prediction using language models from deep learning

2021 ◽  
Author(s):  
Ratul Chowdhury ◽  
Nazim Bouatta ◽  
Surojit Biswas ◽  
Charlotte Rochereau ◽  
George M Church ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Structure Prediction ◽  
Structural Information ◽  
Language Model ◽  
Learning System ◽  
Language Models ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Predict Protein Structure

AlphaFold2 and related systems use deep learning to predict protein structure from co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite dramatic, recent increases in accuracy, three challenges remain: (i) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated, (ii) rapid exploration of designed structures, and (iii) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) able to predict protein structure from single protein sequences without use of MSAs. This deep learning system has two novel elements: a protein language model (AminoBERT) that uses a Transformer to learn latent structural information from millions of unaligned proteins and a geometric module that compactly represents Cα backbone geometry. RGN2 outperforms AlphaFold2 and RoseTTAFold (as well as trRosetta) on orphan proteins and is competitive with designed sequences, while achieving up to a billion-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction.

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