scholarly journals Rethinking protein drug design with highly accurate structure prediction of anti-CRISPR proteins

2021 ◽  
Author(s):  
Ho-min Park ◽  
Yunseol Park ◽  
Joris Vankerschaver ◽  
Arnout Van Messem ◽  
Wesley De Neve ◽  
...  
Keyword(s):  
Protein Structure ◽  
Drug Design ◽  
Structure Prediction ◽  
Modern Medicine ◽  
Protein Therapeutics ◽  
Protein Drug ◽  
Host Proteins ◽  
Small Proteins ◽  
Small Molecule Therapeutics ◽  
Host Parasite

Protein therapeutics play an important role in controlling the functions and activities of disease-causing proteins in modern medicine. Despite protein therapeutics having several advantages over traditional small-molecule therapeutics, further development has been hindered by drug complexity and delivery issues. However, recent progress in deep learning-based protein structure prediction approaches such as AlphaFold opens new opportunities to exploit the complexity of these macro-biomolecules for highly-specialised design to inhibit, regulate or even manipulate specific disease-causing proteins. Anti-CRISPR proteins are small proteins from bacteriophages that counter-defend against the prokaryotic adaptive immunity of CRISPR-Cas systems. They are unique examples of natural protein therapeutics that have been optimized by the host-parasite evolutionary arms race to inhibit a wide variety of host proteins. Here, we show that these Anti-CRISPR proteins display diverse inhibition mechanisms through accurate structural prediction and functional analysis. We find that these phage-derived proteins are extremely distinct in structure, some of which have no homologues in the current protein structure domain. Furthermore, we find a novel family of Anti-CRISPR proteins which are structurally homologous to the recently-discovered mechanism of manipulating host proteins through enzymatic activity, rather than through direct inference. Using highly accurate structure prediction, we present a wide variety of protein-manipulating strategies of anti-CRISPR proteins for future protein drug design.

scholarly journals Bayesian inference of protein structure from chemical shift data

2014 ◽  
Author(s):  
Lars A Bratholm ◽  
Anders Steen Christensen ◽  
Thomas Hamelryck ◽  
Jan H Jensen
Keyword(s):  
Protein Structure ◽  
Chemical Shift ◽  
Probability Distribution ◽  
Structure Prediction ◽  
Calculated Data ◽  
Cauchy Distribution ◽  
Joint Probability Distribution ◽  
Solvent Exposure ◽  
Chemical Shift Prediction ◽  
Small Proteins

Protein chemical shifts are routinely used to augment molecular mechanics force fields in protein structure simulations, with weights of the chemical shift restraints determined empirically. These weights, however, might not be an optimal descriptor of a given protein structure and predictive model, and a bias is introduced which might result in incorrect structures. In the inferential structure determination framework, both the unknown structure and the disagreement between experimental and back-calculated data are formulated as a joint probability distribution, thus utilizing the full information content of the data. Here, we present the formulation of such a probability distribution where the error in chemical shift prediction is described by either a Gaussian or Cauchy distribution. The methodology is demonstrated and compared to a set of empirically weighted potentials through Markov chain Monte Carlo simulations of three small proteins (ENHD, Protein G and the SMN Tudor Domain) using the PROFASI force field and the chemical shift predictor CamShift. Using a clustering-criterion for identifying the best structure, together with the addition of a solvent exposure scoring term, the simulations suggests that sampling both the structure and the uncertainties in chemical shift prediction leads more accurate structures compared to conventional methods using empirical determined weights. The Cauchy distribution, using either sampled uncertainties or predetermined weights, did, however, result in overall better convergence to the native fold, suggesting that both types of distribution might be useful in different aspects of the protein structure prediction.

Metallothionein: Protein structure prediction and sequence analyses in pigeon pea(Cajanuscajan)

2017 ◽  
Vol 4 (04) ◽  
Author(s):  
Sakshi Chaudhary ◽  
Anil Kumar Singh ◽  
Jeshima Khan Yasin
Keyword(s):  
Protein Structure ◽  
Metal Binding ◽  
Structure Prediction ◽  
Structural Similarity ◽  
Pigeon Pea ◽  
Structural Features ◽  
Structural Domain ◽  
Small Proteins ◽  
Heavy Metal Binding ◽  
Entire Sequence

Metallothioneins are a special group of small proteins capable of detoxifying non-essential metal ions present in excess within a plant cell. Metallothioneins are cysteine-rich diverse classes of heavy metal binding protein molecules which are essential for plant growth.These proteins are present in all taxa, except eubacteria. The similarity in protein sequences provides a basis for the method which predicts structural features of a protein with that of a known protein structure. Structural similarity of entire sequence or large sequence fragment enables prediction and modeling of entire structural domain, while distribution of local features of known protein structure make it possible to predict such features in structure of unknown or uncharacterised proteins.In this study, from available genomic resources metallothionein of pigeonpea was identified, structure of metallothionein was predicted and validated. We have presented a step-wise methodology to model a given protein and to validate the structures.

scholarly journals Bayesian inference of protein structure from chemical shift data

2014 ◽  
Author(s):  
Lars A Bratholm ◽  
Anders Steen Christensen ◽  
Thomas Hamelryck ◽  
Jan H Jensen
Keyword(s):  
Protein Structure ◽  
Chemical Shift ◽  
Probability Distribution ◽  
Structure Prediction ◽  
Calculated Data ◽  
Cauchy Distribution ◽  
Joint Probability Distribution ◽  
Solvent Exposure ◽  
Chemical Shift Prediction ◽  
Small Proteins

Protein chemical shifts are routinely used to augment molecular mechanics force fields in protein structure simulations, with weights of the chemical shift restraints determined empirically. These weights, however, might not be an optimal descriptor of a given protein structure and predictive model, and a bias is introduced which might result in incorrect structures. In the inferential structure determination framework, both the unknown structure and the disagreement between experimental and back-calculated data are formulated as a joint probability distribution, thus utilizing the full information content of the data. Here, we present the formulation of such a probability distribution where the error in chemical shift prediction is described by either a Gaussian or Cauchy distribution. The methodology is demonstrated and compared to a set of empirically weighted potentials through Markov chain Monte Carlo simulations of three small proteins (ENHD, Protein G and the SMN Tudor Domain) using the PROFASI force field and the chemical shift predictor CamShift. Using a clustering-criterion for identifying the best structure, together with the addition of a solvent exposure scoring term, the simulations suggests that sampling both the structure and the uncertainties in chemical shift prediction leads more accurate structures compared to conventional methods using empirical determined weights. The Cauchy distribution, using either sampled uncertainties or predetermined weights, did, however, result in overall better convergence to the native fold, suggesting that both types of distribution might be useful in different aspects of the protein structure prediction.

scholarly journals A systematic structural comparison of all solved small proteins (4-6 kDa) reveals the weight of disulfide bonds in proteins' foldability

2021 ◽  
Author(s):  
Mariana Hoyer Moreira ◽  
Fabio C. L. Almeida ◽  
Tatiana Domitrovic ◽  
Fernando L. Palhano
Keyword(s):  
Protein Structure ◽  
Disulfide Bond ◽  
Structure Prediction ◽  
Disulfide Bonds ◽  
Hydrophobic Core ◽  
Structural Comparison ◽  
Small Proteins ◽  
Protein Prediction ◽  
Aqueous Solvent ◽  
Tertiary Protein Structure

Defensins are small proteins, usually ranging from 4 to 6 kDa, amphipathic, disulfide-rich, and with a small or even absent hydrophobic core. Since a hydrophobic core is generally found in globular proteins that fold in an aqueous solvent, the peculiar fold of defensins can challenge tertiary protein structure predictors. We performed a PDB-wide survey of small proteins (4-6 kDa) to understand the similarities of defensins with other small disulfide-rich proteins. We found no differences when we compared defensins with non-defensins regarding the proportion and exposition to the solvent of apolar, polar, and charged residues. Then we divided all small proteins (4-6 kDa) deposited in PDB into two groups, one group with at least one disulfide bond (bonded, defensins included) and another group without any disulfide bond (unbonded). The group of bonded proteins presented apolar residues more exposed to the solvent than the unbonded group. The ab initio algorithm for tertiary protein structure prediction Robetta was more accurate to predict unbonded than bonded proteins. Our work highlights one more layer of complexity for the tertiary protein prediction structure: small disulfide-rich proteins' ability to fold even with a poor hydrophobic core.

Comparing Parallel Algorithms for Van der Waals Energy with Cell-List Technique for Protein Structure Prediction

2018 ◽  
Author(s):  
Daniel R. F. Bonetti ◽  
Gesiel Rios Lopes ◽  
Alexandre C. B. Delbem ◽  
Paulo S. L. Souza ◽  
Kalinka C. Branco ◽  
...  
Keyword(s):  
Protein Structure ◽  
Parallel Algorithms ◽  
Ab Initio ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Van Der Waals ◽  
Parallel Execution ◽  
Programming Models ◽  
Preliminary Results ◽  
Small Proteins

This paper compares the runtime of three distinct parallel algorithms for the evaluation of an ab initio and full-atom approach based on GA and celllist technique, in order to minimize the van der Waals energy. The three parallel algorithms are developed in C and use one of these programming models: MPI, OpenMP or hybrid (MPI+OpenMP). Our preliminary results show that van der Waals Energy are executed faster and with better speedups when using hybrid and more flexible parallel algorithms to predict the structure of larger proteins. We also show that for small proteins the communication of MPI imposes a high overhead for the parallel execution and, thus the OpenMP presents a better relation cost x benefit in such cases.

scholarly journals A comparative study of the reported performance of ab initio protein structure prediction algorithms

2007 ◽  
Vol 5 (21) ◽  
pp. 387-396 ◽  
Author(s):  
Glennie Helles
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Current Knowledge ◽  
Three Dimensional ◽  
Performance Measure ◽  
Prediction Algorithm ◽  
Dimensional Structure ◽  
Prediction Algorithms ◽  
Small Proteins

Protein structure prediction is one of the major challenges in bioinformatics today. Throughout the past five decades, many different algorithmic approaches have been attempted, and although progress has been made the problem remains unsolvable even for many small proteins. While the general objective is to predict the three-dimensional structure from primary sequence, our current knowledge and computational power are simply insufficient to solve a problem of such high complexity. Some prediction algorithms do, however, appear to perform better than others, although it is not always obvious which ones they are and it is perhaps even less obvious why that is. In this review, the reported performance results from 18 different recently published prediction algorithms are compared. Furthermore, the general algorithmic settings most likely responsible for the difference in the reported performance are identified, and the specific settings of each of the 18 prediction algorithms are also compared. The average normalized r.m.s.d. scores reported range from 11.17 to 3.48. With a performance measure including both r.m.s.d. scores and CPU time, the currently best-performing prediction algorithm is identified to be the I-TASSER algorithm. Two of the algorithmic settings—protein representation and fragment assembly—were found to have definite positive influence on the running time and the predicted structures, respectively. There thus appears to be a clear benefit from incorporating this knowledge in the design of new prediction algorithms.

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