Computational Methods for De novo Protein Design and its Applications to the Human Immunodeficiency Virus 1, Purine Nucleoside Phosphorylase, Ubiquitin Specific Protease 7, and Histone Demethylases

Bellows, Meghan L.; Floudas, Christodoulos A.

doi:10.2174/138945010790711914

Cited by 18 publications

(12 citation statements)

References 206 publications

(210 reference statements)

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“…Knowledge‐based potential functions are used in many different types of computational protein studies, including protein structure prediction,1–5 protein design,6–9 docking applications,10–13 and protein folding mechanism studies 14–17. Many atomistic potential functions18–20 and coarse‐grained potential functions21–24 have been developed.…”

Section: Introductionmentioning

confidence: 99%

Multibody coarse‐grained potentials for native structure recognition and quality assessment of protein models

et al. 2011

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Summary Multi-body potentials have been of much interest recently because they take into account three dimensional interactions related to residue packing and capture the cooperativity of these interactions in protein structures. Our goal was to combine long range multi-body potentials and short range potentials to improve recognition of native structure among misfolded decoys. We optimized the weights for four-body non-sequential, four-body sequential and short range potentials in order to obtain optimal model ranking results for threading and have compared these data against results obtained with other potentials. (Twenty six different coarse-grained potentials from the Potentials ‘R’Us web server have been used.) Our optimized multi-body potentials outperform all other contact potentials in the recognition of the native structure among decoys, both for models from homology template-based modeling and from template-free modeling in CASP8 decoy sets. We have compared the results obtained for this optimized coarse-grained potentials, where each residue is represented by a single point, with results obtained by using the DFIRE potential, which takes into account atomic level information of proteins. We found that for all proteins larger than 80 amino acids our optimized coarse-grained potentials yield results comparable to those obtained with the atomic DFIRE potential.

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Section: Introductionmentioning

confidence: 99%

Multibody coarse‐grained potentials for native structure recognition and quality assessment of protein models

et al. 2011

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“…Computational Protein Design (CPD) enables systematic, high throughput protein, and ligand mutagenesis and has been the focus of several laboratories in recent years, resulting in significant developments in methodology and applications 1–13. CPD has been used to modify specificity,14–17 to improve protein‐ligand binding,18–24 to increase stability,25, 26 to stabilize novel or alternative protein folds,27, 28 to perform fold recognition and homology searching,5, 11 to design new proteins,29, 30 and enzyme active sites,31, 32 to optimize ligand entrance and escape pathways,33 to create water‐soluble variants of membrane proteins,34 to redesign protein‐protein interfaces,20, 22, 35, 36 and to rewire biological networks 37…”

Section: Introductionmentioning

confidence: 99%

Computational protein design with a generalized born solvent model: Application to asparaginyl‐tRNA synthetase

et al. 2011

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Computational Protein Design (CPD) is a promising method for high throughput protein and ligand mutagenesis. Recently, we developed a CPD method that used a polar-hydrogen energy function for protein interactions and a Coulomb/Accessible Surface Area (CASA) model for solvent effects. We applied this method to engineer aspartyl-adenylate (AspAMP) specificity into Asparaginyl-tRNA synthetase (AsnRS), whose substrate is asparaginyl-adenylate (AsnAMP). Here, we implement a more accurate function, with an all-atom energy for protein interactions and a residue-pairwise generalized Born model for solvent effects. As a first test, we compute aminoacid affinities for several point mutants of Aspartyl-tRNA synthetase (AspRS) and Tyrosyl-tRNA synthetase and stability changes for three helical peptides and compare with experiment. As a second test, we readdress the problem of AsnRS aminoacid engineering. We compare three design criteria, which optimize the folding free-energy, the absolute AspAMP affinity, and the relative (AspAMP-AsnAMP) affinity. The sequences and conformations are improved with respect to our previous, polar-hydrogen/CASA study: For several designed complexes, the AspAMP carboxylate forms three interactions with a conserved arginine and a designed lysine, as in the active site of the AspRS:AspAMP complex. The conformations and interactions are well maintained in molecular dynamics simulations and the sequences have an inverted specificity, favoring AspAMP over AsnAMP. The method is not fully successful, since experimental measurements with the seven most promising sequences show that they do not catalyze at a detectable level the adenylation of Asp (or Asn) with ATP. This may be due to weak AspAMP binding and/or disruption of transition-state stabilization.

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“…In some cases, particularly with the linear α-helices, this may be poor assumption. In fact, recent work suggests that optimizing a sequence to stabilize a conformation is a viable option to improving potency [36][37][38][39][40][41][42][43].…”

Section: Methodsmentioning

confidence: 99%

Rigorous Computational and Experimental Investigations on MDM2/MDMX-Targeted Linear and Macrocyclic Peptides

et al. 2019

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There is interest in peptide drug design, especially for targeting intracellular protein–protein interactions. Therefore, the experimental validation of a computational platform for enabling peptide drug design is of interest. Here, we describe our peptide drug design platform (CMDInventus) and demonstrate its use in modeling and predicting the structural and binding aspects of diverse peptides that interact with oncology targets MDM2/MDMX in comparison to both retrospective (pre-prediction) and prospective (post-prediction) data. In the retrospective study, CMDInventus modules (CMDpeptide, CMDboltzmann, CMDescore and CMDyscore) were used to accurately reproduce structural and binding data across multiple MDM2/MDMX data sets. In the prospective study, CMDescore, CMDyscore and CMDboltzmann were used to accurately predict binding affinities for an Ala-scan of the stapled α-helical peptide ATSP-7041. Remarkably, CMDboltzmann was used to accurately predict the results of a novel D-amino acid scan of ATSP-7041. Our investigations rigorously validate CMDInventus and support its utility for enabling peptide drug design.

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Computational Methods for De novo Protein Design and its Applications to the Human Immunodeficiency Virus 1, Purine Nucleoside Phosphorylase, Ubiquitin Specific Protease 7, and Histone Demethylases

Cited by 18 publications

References 206 publications

Multibody coarse‐grained potentials for native structure recognition and quality assessment of protein models

Multibody coarse‐grained potentials for native structure recognition and quality assessment of protein models

Computational protein design with a generalized born solvent model: Application to asparaginyl‐tRNA synthetase

Rigorous Computational and Experimental Investigations on MDM2/MDMX-Targeted Linear and Macrocyclic Peptides

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