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Yaoqi Zhou - One of the best experts on this subject based on the ideXlab platform.
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dligand2 an improved knowledge based energy function for protein ligand interactions using the distance scaled finite ideal gas reference state
Journal of Cheminformatics, 2019Co-Authors: Pin Chen, Yaoqi Zhou, Hui Yan, Huiying Zhao, Yuedong YangAbstract:Performance of structure-based molecular docking largely depends on the accuracy of scoring functions. One important type of scoring functions are knowledge-based Potentials derived from known three-dimensional structures of proteins and/or protein–ligand complex structures. This study seeks to improve a knowledge-based protein–ligand Potential based on a distance-scale finite ideal-gas reference (DFIRE) state (DLIGAND) by expanding the representation of protein atoms from 13 mol2 atom types to 167 residue-specific atom types, and employing a recently updated dataset containing 12,450 monomer protein chains for training. We found that the updated version DLIGAND2 has a consistent improvement over DLIGAND in predicting binding affinities for either native complex structures or docking-generated poses. More importantly, DLIGAND2 has a 52% increase over DLIGAND in enrichment factors in top 1% predictions based on the DUD-E decoy set, and consistently improves over Autodock Vina and other Statistical energy functions in all three benchmark tests. We further found that DLIGAND2 outperforms empirical and machine-learning methods compared for virtual screening on new targets that are not homologous to the DUD-E training set. Given the best performance as a parameter-free Statistical Potential and among the best in all performance measures, DLIGAND2 should be useful for re-assessing the poses generated by docking software, or acting as one term in other scoring functions. The program is available at https://github.com/sysu-yanglab/DLIGAND2 .
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an all atom knowledge based energy function for protein dna threading docking decoy discrimination and prediction of transcription factor binding profiles
Proteins, 2009Co-Authors: Yaoqi Zhou, Yuedong Yang, Haojun LiangAbstract:How to make an accurate representation of protein-DNA interaction by an energy function is a long-standing unsolved problem in structural biology. Here, we modified a Statistical Potential based on the distancescaled, finite ideal-gas reference state so that it is optimized for protein-DNA interactions. The changes include a volume-fraction correction to account for unmixable atom types in proteins and DNA in addition to the usage of a low-count correction, residue/base-specific atom types, and a shorter cutoff distance for protein-DNA interactions. The new Statistical energy functions are tested in threading and docking decoy discriminations and prediction of protein-DNA binding affinities and transcriptionfactor binding profiles. The results indicate that new proposed energy functions are among the best in existing energy functions for protein-DNA interactions. The new energy functions are available as a web-server called DDNA 2.0 at http://sparks. informatics.iupui.edu. The server version was trained by the entire 212 protein-DNA complexes.
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distance scaled finite ideal gas reference state improves structure derived Potentials of mean force for structure selection and stability prediction
Protein Science, 2009Co-Authors: Hongyi Zhou, Yaoqi ZhouAbstract:The solution of the protein folding problem requires an accurate Potential that describes the interactions among different amino acid residues. The Potential that would yield a complete understanding of the folding phenomena should be derived from the laws of physics. However, the use of such physical-based Potentials (Brooks et al. 1983; Weiner et al. 1986; Jorgensen et al. 1996; Scott et al.1999) for ab initio folding studies is limited by available computing power (Duan and Kollman 1998). Their applications to the recognition of native structures from nonnative conformations (Moult 1997; Hao and Scheraga 1998; Lazaridis and Karplus 2000; Petrey and Honig 2000; Wallqvist et al. 2002), however, yielded results comparable to knowledge-based Statistical Potentials that extract interactions directly from known protein structures (Tanaka and Scheraga 1976). Knowledge-based Statistical Potentials are attractive because they are simple and easy to use. Knowledge-based Potentials can be categorized into distance-independent contact energies (Miyazawa and Jernigan 1985; DeBolt and Skolnick 1996; Zhang et al. 1997; Skolnick et al. 2000) and distance-dependent Potentials (Hendlich et al. 1990; Sippl 1990; Jones et al. 1992; Samudrala and Moult 1998; Lu and Skolnick 2001). Both residue level (Miyazawa and Jernigan 1985; Hendlich et al. 1990; Sippl 1990; Jones et al. 1992) and atomic level (DeBolt and Skolnick 1996; Zhang et al. 1997; Samudrala and Moult 1998; Lu and Skolnick 2001) Potentials were developed and applied to fold recognition and assessment (Hendlich et al. 1990; Sippl 1990; Casari and Sippl 1992; Jones et al. 1992; Bryant and Lawrence 1993; Samudrala and Moult 1998; Miyazawa and Jernigan 1999; Lu and Skolnick 2001; Melo et al. 2002), structure predictions (Sun 1993; Simons et al. 1997; Skolnick et al. 1997; Lee et al. 1999; Tobi and Elber 2000; Vendruscolo et al. 2000; Pillardy et al. 2001), and validations (Luthy et al. 1992; Sippl 1993; MacArthur et al. 1994; Rojnuckarin and Subramaniam 1999), docking and binding (Pellegrini et al. 1995; Wallqvist et al. 1995; Zhang et al. 1997), and mutation-induced changes in stability (Gilis and Rooman 1996,Gilis and Rooman 1997; Zhang et al. 1997). This work focuses on distance-dependent, residue-specific, all-atom, knowledge-based Potentials. This is because in protein–structure selections, all-atom–based Potentials perform better than residue-based Potentials (Samudrala and Moult 1998; Lu and Skolnick 2001), and distance-dependent Potentials better than distance-independent ones (Melo et al. 2002). The derivation of a distance-dependent, pairwise, Statistical Potential (i,j,r) starts from a common equation given by (1) where R is the gas constant, T is the temperature, Nobs(i,j,r) is the observed number of atomic pairs (i,j) within a distance shell r − Δr/2 to r + Δr/2 in a database of folded structures, and Nexp(i,j,r) is the expected number of atomic pairs (i,j) in the same distance shell if there were no interactions between atoms (the reference state). Clearly, the method used to calculate Nexp(i,j,r) is what makes one Potential differ from another because the method to calculate Nobs(i,j,r) is the same (except minor differences in database and bin procedures). Samudrala and Moult (1998) used a conditional probability function (2) where Nobs(r) ≡ ∑i,j Nobs(i,j,r), Nobs(i,j) ≡ ∑r Nobs(i,j,r) and Ntotal ≡ ∑i,j,r Nobs(i,j,r). Lu and Skolnick (2001) employed a quasi-chemical approximation: (3) where χk is the mole fraction of atom type k. The common approximation made by the above two Potentials is that ∑i,j Nexp(i,j,r) ≡ Nobs(r). This approximation has its origin in the "uniform density" reference state used by Sippl (1990) to derive the residue-based, distance-dependent Potential. In this approximation, the total number of pairs in any given distance shell for a reference state is the same as that for folded proteins. In other words, the distance dependence of the pair probability distribution of the reference state is an averaged distribution over all residue or atomic pairs. This reference state is a noninteracting ideal-gas reference state only if the average interaction of all residue or atomic pairs is zero (i.e., attractive and repulsive interactions cancel each other). However, it is highly unlikely that attractive and repulsive interactions could cancel each other exactly. These missing residual interactions may well be important for an accurate Potential. To explore the missing residual interactions, we establish a noninteracting reference state without using the above-mentioned assumption. This is done by using uniformly distributed noninteracting points in finite spheres. The reference state coupled with a simple distance scaling method is employed to derive an all-atom Potential of mean force from 1011 known protein structures (Hobohm et al. 1992). It is shown that the new atomic Potential is slightly more attractive than other knowledge-based all-atom Potentials (Samudrala and Moult 1998; Lu and Skolnick 2001). This small residual interaction leads to an improved Potential of mean force for structure selections from single and multiple decoy sets and for the prediction of the changes in the stabilities of 895 mutants.
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a physical reference state unifies the structure derived Potential of mean force for protein folding and binding
Proteins, 2004Co-Authors: Song Liu, Hongyi Zhou, Chi Zhang, Yaoqi ZhouAbstract:Extracting knowledge-based Statistical Potential from known structures of proteins is proved to be a simple, effective method to obtain an approximate free-energy function. However, the different compositions of amino acid residues at the core, the surface, and the binding interface of proteins prohibited the establishment of a unified Statistical Potential for folding and binding despite the fact that the physical basis of the interaction (water-mediated interaction between amino acids) is the same. Recently, a physical state of ideal gas, rather than a Statistically averaged state, has been used as the reference state for extracting the net interaction energy between amino acid residues of monomeric proteins. Here, we find that this monomer-based Potential is more accurate than an existing all-atom knowledge-based Potential trained with interfacial structures of dimers in distinguishing native complex structures from docking decoys (100% success rate vs. 52% in 21 dimer/trimer decoy sets). It is also more accurate than a recently developed semiphysical empirical free-energy functional enhanced by an orientation-dependent hydrogen-bonding Potential in distinguishing native state from Rosetta docking decoys (94% success rate vs. 74% in 31 antibody-antigen and other complexes based on Z score). In addition, the monomer Potential achieved a 93% success rate in distinguishing true dimeric interfaces from artificial crystal interfaces. More importantly, without additional parameters, the Potential provides an accurate prediction of binding free energy of protein-peptide and protein-protein complexes (a correlation coefficient of 0.87 and a root-mean-square deviation of 1.76 kcal/mol with 69 experimental data points). This work marks a significant step toward a unified knowledge-based Potential that quantitatively captures the common physical principle underlying folding and binding. A Web server for academic users, established for the prediction of binding free energy and the energy evaluation of the protein-protein complexes, may be found at http://theory.med.buffalo.edu.
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a physical reference state unifies the structure derived Potential of mean force for protein folding and binding
Proteins, 2004Co-Authors: Song Liu, Hongyi Zhou, Chi Zhang, Yaoqi ZhouAbstract:Extracting knowledge-based Statistical Potential from known structures of proteins is proved to be a simple, effective method to obtain an approximate free-energy function. However, the different compositions of amino acid residues at the core, the surface, and the binding interface of proteins prohibited the establishment of a unified Statistical Potential for folding and binding despite the fact that the physical basis of the interaction (water-mediated interaction between amino acids) is the same. Recently, a physical state of ideal gas, rather than a Statistically averaged state, has been used as the reference state for extracting the net interaction energy between amino acid residues of monomeric proteins. Here, we find that this monomer-based Potential is more accurate than an existing all-atom knowledge-based Potential trained with interfacial structures of dimers in distinguishing native complex structures from docking decoys (100% success rate vs. 52% in 21 dimer/trimer decoy sets). It is also more accurate than a recently developed semiphysical empirical free-energy functional enhanced by an orientation-dependent hydrogen-bonding Potential in distinguishing native state from Rosetta docking decoys (94% success rate vs. 74% in 31 antibody–antigen and other complexes based on Z score). In addition, the monomer Potential achieved a 93% success rate in distinguishing true dimeric interfaces from artificial crystal interfaces. More importantly, without additional parameters, the Potential provides an accurate prediction of binding free energy of protein–peptide and protein–protein complexes (a correlation coefficient of 0.87 and a root-mean-square deviation of 1.76 kcal/mol with 69 experimental data points). This work marks a significant step toward a unified knowledge-based Potential that quantitatively captures the common physical principle underlying folding and binding. A Web server for academic users, established for the prediction of binding free energy and the energy evaluation of the protein–protein complexes, may be found at http://theory.med.buffalo.edu. Proteins 2004. © 2004 Wiley-Liss, Inc.
Amy E. Keating - One of the best experts on this subject based on the ideXlab platform.
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a set of computationally designed orthogonal antiparallel homodimers that expands the synthetic coiled coil toolkit
Journal of the American Chemical Society, 2014Co-Authors: Christopher Negron, Amy E. KeatingAbstract:Molecular engineering of protein assemblies, including the fabrication of nanostructures and synthetic signaling pathways, relies on the availability of modular parts that can be combined to give different structures and functions. Currently, a limited number of well-characterized protein interaction components are available. Coiled-coil interaction modules have been demonstrated to be useful for biomolecular design, and many parallel homodimers and heterodimers are available in the coiled-coil toolkit. In this work, we sought to design a set of orthogonal antiparallel homodimeric coiled coils using a computational approach. There are very few antiparallel homodimers described in the literature, and none have been measured for cross-reactivity. We tested the ability of the distance-dependent Statistical Potential DFIRE to predict orientation preferences for coiled-coil dimers of known structure. The DFIRE model was then combined with the CLASSY multistate protein design framework to engineer sets of three...
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a set of computationally designed orthogonal antiparallel homodimers that expands the synthetic coiled coil toolkit
Journal of the American Chemical Society, 2014Co-Authors: Christopher Negron, Amy E. KeatingAbstract:Molecular engineering of protein assemblies, including the fabrication of nanostructures and synthetic signaling pathways, relies on the availability of modular parts that can be combined to give different structures and functions. Currently, a limited number of well-characterized protein interaction components are available. Coiled-coil interaction modules have been demonstrated to be useful for biomolecular design, and many parallel homodimers and heterodimers are available in the coiled-coil toolkit. In this work, we sought to design a set of orthogonal antiparallel homodimeric coiled coils using a computational approach. There are very few antiparallel homodimers described in the literature, and none have been measured for cross-reactivity. We tested the ability of the distance-dependent Statistical Potential DFIRE to predict orientation preferences for coiled-coil dimers of known structure. The DFIRE model was then combined with the CLASSY multistate protein design framework to engineer sets of three orthogonal antiparallel homodimeric coiled coils. Experimental measurements confirmed the successful design of three peptides that preferentially formed antiparallel homodimers that, furthermore, did not interact with one additional previously reported antiparallel homodimer. Two designed peptides that formed higher-order structures suggest how future design protocols could be improved. The successful designs represent a significant expansion of the existing protein-interaction toolbox for molecular engineers.
Hongyi Zhou - One of the best experts on this subject based on the ideXlab platform.
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goap a generalized orientation dependent all atom Statistical Potential for protein structure prediction
Biophysical Journal, 2011Co-Authors: Hongyi Zhou, Jeffrey SkolnickAbstract:An accurate scoring function is a key component for successful protein structure prediction. To address this important unsolved problem, we develop a generalized orientation and distance-dependent all-atom Statistical Potential. The new Statistical Potential, generalized orientation-dependent all-atom Potential (GOAP), depends on the relative orientation of the planes associated with each heavy atom in interacting pairs. GOAP is a generalization of previous orientation-dependent Potentials that consider only representative atoms or blocks of side-chain or polar atoms. GOAP is decomposed into distance- and angle-dependent contributions. The DFIRE distance-scaled finite ideal gas reference state is employed for the distance-dependent component of GOAP. GOAP was tested on 11 commonly used decoy sets containing 278 targets, and recognized 226 native structures as best from the decoys, whereas DFIRE recognized 127 targets. The major improvement comes from decoy sets that have homology-modeled structures that are close to native (all within ∼4.0 A) or from the ROSETTA ab initio decoy set. For these two kinds of decoys, orientation-independent DFIRE or only side-chain orientation-dependent RWplus performed poorly. Although the OPUS-PSP block-based orientation-dependent, side-chain atom contact Potential performs much better (recognizing 196 targets) than DFIRE, RWplus, and dDFIRE, it is still ∼15% worse than GOAP. Thus, GOAP is a promising advance in knowledge-based, all-atom Statistical Potentials. GOAP is available for download at http://cssb.biology.gatech.edu/GOAP.
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goap a generalized orientation dependent all atom Statistical Potential for protein structure prediction
Biophysical Journal, 2011Co-Authors: Hongyi Zhou, Jeffrey SkolnickAbstract:An accurate scoring function is a key component for successful protein structure prediction. To address this important unsolved problem, we develop a generalized orientation and distance-dependent all-atom Statistical Potential. The new Statistical Potential, generalized orientation-dependent all-atom Potential (GOAP), depends on the relative orientation of the planes associated with each heavy atom in interacting pairs. GOAP is a generalization of previous orientation-dependent Potentials that consider only representative atoms or blocks of side-chain or polar atoms. GOAP is decomposed into distance- and angle-dependent contributions. The DFIRE distance-scaled finite ideal gas reference state is employed for the distance-dependent component of GOAP. GOAP was tested on 11 commonly used decoy sets containing 278 targets, and recognized 226 native structures as best from the decoys, whereas DFIRE recognized 127 targets. The major improvement comes from decoy sets that have homology-modeled structures that are close to native (all within ∼4.0 A) or from the ROSETTA ab initio decoy set. For these two kinds of decoys, orientation-independent DFIRE or only side-chain orientation-dependent RWplus performed poorly. Although the OPUS-PSP block-based orientation-dependent, side-chain atom contact Potential performs much better (recognizing 196 targets) than DFIRE, RWplus, and dDFIRE, it is still ∼15% worse than GOAP. Thus, GOAP is a promising advance in knowledge-based, all-atom Statistical Potentials. GOAP is available for download at http://cssb.biology.gatech.edu/GOAP.
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distance scaled finite ideal gas reference state improves structure derived Potentials of mean force for structure selection and stability prediction
Protein Science, 2009Co-Authors: Hongyi Zhou, Yaoqi ZhouAbstract:The solution of the protein folding problem requires an accurate Potential that describes the interactions among different amino acid residues. The Potential that would yield a complete understanding of the folding phenomena should be derived from the laws of physics. However, the use of such physical-based Potentials (Brooks et al. 1983; Weiner et al. 1986; Jorgensen et al. 1996; Scott et al.1999) for ab initio folding studies is limited by available computing power (Duan and Kollman 1998). Their applications to the recognition of native structures from nonnative conformations (Moult 1997; Hao and Scheraga 1998; Lazaridis and Karplus 2000; Petrey and Honig 2000; Wallqvist et al. 2002), however, yielded results comparable to knowledge-based Statistical Potentials that extract interactions directly from known protein structures (Tanaka and Scheraga 1976). Knowledge-based Statistical Potentials are attractive because they are simple and easy to use. Knowledge-based Potentials can be categorized into distance-independent contact energies (Miyazawa and Jernigan 1985; DeBolt and Skolnick 1996; Zhang et al. 1997; Skolnick et al. 2000) and distance-dependent Potentials (Hendlich et al. 1990; Sippl 1990; Jones et al. 1992; Samudrala and Moult 1998; Lu and Skolnick 2001). Both residue level (Miyazawa and Jernigan 1985; Hendlich et al. 1990; Sippl 1990; Jones et al. 1992) and atomic level (DeBolt and Skolnick 1996; Zhang et al. 1997; Samudrala and Moult 1998; Lu and Skolnick 2001) Potentials were developed and applied to fold recognition and assessment (Hendlich et al. 1990; Sippl 1990; Casari and Sippl 1992; Jones et al. 1992; Bryant and Lawrence 1993; Samudrala and Moult 1998; Miyazawa and Jernigan 1999; Lu and Skolnick 2001; Melo et al. 2002), structure predictions (Sun 1993; Simons et al. 1997; Skolnick et al. 1997; Lee et al. 1999; Tobi and Elber 2000; Vendruscolo et al. 2000; Pillardy et al. 2001), and validations (Luthy et al. 1992; Sippl 1993; MacArthur et al. 1994; Rojnuckarin and Subramaniam 1999), docking and binding (Pellegrini et al. 1995; Wallqvist et al. 1995; Zhang et al. 1997), and mutation-induced changes in stability (Gilis and Rooman 1996,Gilis and Rooman 1997; Zhang et al. 1997). This work focuses on distance-dependent, residue-specific, all-atom, knowledge-based Potentials. This is because in protein–structure selections, all-atom–based Potentials perform better than residue-based Potentials (Samudrala and Moult 1998; Lu and Skolnick 2001), and distance-dependent Potentials better than distance-independent ones (Melo et al. 2002). The derivation of a distance-dependent, pairwise, Statistical Potential (i,j,r) starts from a common equation given by (1) where R is the gas constant, T is the temperature, Nobs(i,j,r) is the observed number of atomic pairs (i,j) within a distance shell r − Δr/2 to r + Δr/2 in a database of folded structures, and Nexp(i,j,r) is the expected number of atomic pairs (i,j) in the same distance shell if there were no interactions between atoms (the reference state). Clearly, the method used to calculate Nexp(i,j,r) is what makes one Potential differ from another because the method to calculate Nobs(i,j,r) is the same (except minor differences in database and bin procedures). Samudrala and Moult (1998) used a conditional probability function (2) where Nobs(r) ≡ ∑i,j Nobs(i,j,r), Nobs(i,j) ≡ ∑r Nobs(i,j,r) and Ntotal ≡ ∑i,j,r Nobs(i,j,r). Lu and Skolnick (2001) employed a quasi-chemical approximation: (3) where χk is the mole fraction of atom type k. The common approximation made by the above two Potentials is that ∑i,j Nexp(i,j,r) ≡ Nobs(r). This approximation has its origin in the "uniform density" reference state used by Sippl (1990) to derive the residue-based, distance-dependent Potential. In this approximation, the total number of pairs in any given distance shell for a reference state is the same as that for folded proteins. In other words, the distance dependence of the pair probability distribution of the reference state is an averaged distribution over all residue or atomic pairs. This reference state is a noninteracting ideal-gas reference state only if the average interaction of all residue or atomic pairs is zero (i.e., attractive and repulsive interactions cancel each other). However, it is highly unlikely that attractive and repulsive interactions could cancel each other exactly. These missing residual interactions may well be important for an accurate Potential. To explore the missing residual interactions, we establish a noninteracting reference state without using the above-mentioned assumption. This is done by using uniformly distributed noninteracting points in finite spheres. The reference state coupled with a simple distance scaling method is employed to derive an all-atom Potential of mean force from 1011 known protein structures (Hobohm et al. 1992). It is shown that the new atomic Potential is slightly more attractive than other knowledge-based all-atom Potentials (Samudrala and Moult 1998; Lu and Skolnick 2001). This small residual interaction leads to an improved Potential of mean force for structure selections from single and multiple decoy sets and for the prediction of the changes in the stabilities of 895 mutants.
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a physical reference state unifies the structure derived Potential of mean force for protein folding and binding
Proteins, 2004Co-Authors: Song Liu, Hongyi Zhou, Chi Zhang, Yaoqi ZhouAbstract:Extracting knowledge-based Statistical Potential from known structures of proteins is proved to be a simple, effective method to obtain an approximate free-energy function. However, the different compositions of amino acid residues at the core, the surface, and the binding interface of proteins prohibited the establishment of a unified Statistical Potential for folding and binding despite the fact that the physical basis of the interaction (water-mediated interaction between amino acids) is the same. Recently, a physical state of ideal gas, rather than a Statistically averaged state, has been used as the reference state for extracting the net interaction energy between amino acid residues of monomeric proteins. Here, we find that this monomer-based Potential is more accurate than an existing all-atom knowledge-based Potential trained with interfacial structures of dimers in distinguishing native complex structures from docking decoys (100% success rate vs. 52% in 21 dimer/trimer decoy sets). It is also more accurate than a recently developed semiphysical empirical free-energy functional enhanced by an orientation-dependent hydrogen-bonding Potential in distinguishing native state from Rosetta docking decoys (94% success rate vs. 74% in 31 antibody-antigen and other complexes based on Z score). In addition, the monomer Potential achieved a 93% success rate in distinguishing true dimeric interfaces from artificial crystal interfaces. More importantly, without additional parameters, the Potential provides an accurate prediction of binding free energy of protein-peptide and protein-protein complexes (a correlation coefficient of 0.87 and a root-mean-square deviation of 1.76 kcal/mol with 69 experimental data points). This work marks a significant step toward a unified knowledge-based Potential that quantitatively captures the common physical principle underlying folding and binding. A Web server for academic users, established for the prediction of binding free energy and the energy evaluation of the protein-protein complexes, may be found at http://theory.med.buffalo.edu.
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a physical reference state unifies the structure derived Potential of mean force for protein folding and binding
Proteins, 2004Co-Authors: Song Liu, Hongyi Zhou, Chi Zhang, Yaoqi ZhouAbstract:Extracting knowledge-based Statistical Potential from known structures of proteins is proved to be a simple, effective method to obtain an approximate free-energy function. However, the different compositions of amino acid residues at the core, the surface, and the binding interface of proteins prohibited the establishment of a unified Statistical Potential for folding and binding despite the fact that the physical basis of the interaction (water-mediated interaction between amino acids) is the same. Recently, a physical state of ideal gas, rather than a Statistically averaged state, has been used as the reference state for extracting the net interaction energy between amino acid residues of monomeric proteins. Here, we find that this monomer-based Potential is more accurate than an existing all-atom knowledge-based Potential trained with interfacial structures of dimers in distinguishing native complex structures from docking decoys (100% success rate vs. 52% in 21 dimer/trimer decoy sets). It is also more accurate than a recently developed semiphysical empirical free-energy functional enhanced by an orientation-dependent hydrogen-bonding Potential in distinguishing native state from Rosetta docking decoys (94% success rate vs. 74% in 31 antibody–antigen and other complexes based on Z score). In addition, the monomer Potential achieved a 93% success rate in distinguishing true dimeric interfaces from artificial crystal interfaces. More importantly, without additional parameters, the Potential provides an accurate prediction of binding free energy of protein–peptide and protein–protein complexes (a correlation coefficient of 0.87 and a root-mean-square deviation of 1.76 kcal/mol with 69 experimental data points). This work marks a significant step toward a unified knowledge-based Potential that quantitatively captures the common physical principle underlying folding and binding. A Web server for academic users, established for the prediction of binding free energy and the energy evaluation of the protein–protein complexes, may be found at http://theory.med.buffalo.edu. Proteins 2004. © 2004 Wiley-Liss, Inc.
Christopher Negron - One of the best experts on this subject based on the ideXlab platform.
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a set of computationally designed orthogonal antiparallel homodimers that expands the synthetic coiled coil toolkit
Journal of the American Chemical Society, 2014Co-Authors: Christopher Negron, Amy E. KeatingAbstract:Molecular engineering of protein assemblies, including the fabrication of nanostructures and synthetic signaling pathways, relies on the availability of modular parts that can be combined to give different structures and functions. Currently, a limited number of well-characterized protein interaction components are available. Coiled-coil interaction modules have been demonstrated to be useful for biomolecular design, and many parallel homodimers and heterodimers are available in the coiled-coil toolkit. In this work, we sought to design a set of orthogonal antiparallel homodimeric coiled coils using a computational approach. There are very few antiparallel homodimers described in the literature, and none have been measured for cross-reactivity. We tested the ability of the distance-dependent Statistical Potential DFIRE to predict orientation preferences for coiled-coil dimers of known structure. The DFIRE model was then combined with the CLASSY multistate protein design framework to engineer sets of three...
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a set of computationally designed orthogonal antiparallel homodimers that expands the synthetic coiled coil toolkit
Journal of the American Chemical Society, 2014Co-Authors: Christopher Negron, Amy E. KeatingAbstract:Molecular engineering of protein assemblies, including the fabrication of nanostructures and synthetic signaling pathways, relies on the availability of modular parts that can be combined to give different structures and functions. Currently, a limited number of well-characterized protein interaction components are available. Coiled-coil interaction modules have been demonstrated to be useful for biomolecular design, and many parallel homodimers and heterodimers are available in the coiled-coil toolkit. In this work, we sought to design a set of orthogonal antiparallel homodimeric coiled coils using a computational approach. There are very few antiparallel homodimers described in the literature, and none have been measured for cross-reactivity. We tested the ability of the distance-dependent Statistical Potential DFIRE to predict orientation preferences for coiled-coil dimers of known structure. The DFIRE model was then combined with the CLASSY multistate protein design framework to engineer sets of three orthogonal antiparallel homodimeric coiled coils. Experimental measurements confirmed the successful design of three peptides that preferentially formed antiparallel homodimers that, furthermore, did not interact with one additional previously reported antiparallel homodimer. Two designed peptides that formed higher-order structures suggest how future design protocols could be improved. The successful designs represent a significant expansion of the existing protein-interaction toolbox for molecular engineers.
Yuedong Yang - One of the best experts on this subject based on the ideXlab platform.
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dligand2 an improved knowledge based energy function for protein ligand interactions using the distance scaled finite ideal gas reference state
Journal of Cheminformatics, 2019Co-Authors: Pin Chen, Yaoqi Zhou, Hui Yan, Huiying Zhao, Yuedong YangAbstract:Performance of structure-based molecular docking largely depends on the accuracy of scoring functions. One important type of scoring functions are knowledge-based Potentials derived from known three-dimensional structures of proteins and/or protein–ligand complex structures. This study seeks to improve a knowledge-based protein–ligand Potential based on a distance-scale finite ideal-gas reference (DFIRE) state (DLIGAND) by expanding the representation of protein atoms from 13 mol2 atom types to 167 residue-specific atom types, and employing a recently updated dataset containing 12,450 monomer protein chains for training. We found that the updated version DLIGAND2 has a consistent improvement over DLIGAND in predicting binding affinities for either native complex structures or docking-generated poses. More importantly, DLIGAND2 has a 52% increase over DLIGAND in enrichment factors in top 1% predictions based on the DUD-E decoy set, and consistently improves over Autodock Vina and other Statistical energy functions in all three benchmark tests. We further found that DLIGAND2 outperforms empirical and machine-learning methods compared for virtual screening on new targets that are not homologous to the DUD-E training set. Given the best performance as a parameter-free Statistical Potential and among the best in all performance measures, DLIGAND2 should be useful for re-assessing the poses generated by docking software, or acting as one term in other scoring functions. The program is available at https://github.com/sysu-yanglab/DLIGAND2 .
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an all atom knowledge based energy function for protein dna threading docking decoy discrimination and prediction of transcription factor binding profiles
Proteins, 2009Co-Authors: Yaoqi Zhou, Yuedong Yang, Haojun LiangAbstract:How to make an accurate representation of protein-DNA interaction by an energy function is a long-standing unsolved problem in structural biology. Here, we modified a Statistical Potential based on the distancescaled, finite ideal-gas reference state so that it is optimized for protein-DNA interactions. The changes include a volume-fraction correction to account for unmixable atom types in proteins and DNA in addition to the usage of a low-count correction, residue/base-specific atom types, and a shorter cutoff distance for protein-DNA interactions. The new Statistical energy functions are tested in threading and docking decoy discriminations and prediction of protein-DNA binding affinities and transcriptionfactor binding profiles. The results indicate that new proposed energy functions are among the best in existing energy functions for protein-DNA interactions. The new energy functions are available as a web-server called DDNA 2.0 at http://sparks. informatics.iupui.edu. The server version was trained by the entire 212 protein-DNA complexes.