CABS‐NMR—De novo tool for rapid global fold determination from chemical shifts,residual dipolar couplings and sparse methyl‐methyl noes

Recent development of nuclear magnetic resonance (NMR) techniques provided new types of structural restraints that can be successfully used in fast and low‐cost global protein fold determination. Here, we present CABS‐NMR, an efficient protein modeling tool, which takes advantage of such structural restraints. The restraints are converted from original NMR data to fit the coarse grained protein representation of the C‐Alpha‐Beta‐Side‐group (CABS) algorithm. CABS is a Monte Carlo search algorithm that uses a knowledge‐based force field. Its versatile structure enables a variety of protein‐modeling protocols, including purely de novo folding, folding guided by restraints derived from template structures or, structure assembly based on experimental data. In particular, CABS‐NMR uses the distance and angular restraints set derived from various NMR experiments. This new modeling technique was successfully tested in structure determination of 10 globular proteins of size up to 216 residues, for which sparse NMR data were available. Additional detailed analysis was performed for a S100A1 protein. Namely, we successfully predicted Nuclear Overhauser Effect signals on the basis of low‐energy structures obtained from chemical shifts by CABS‐NMR. It has been observed that utility of chemical shifts and other types of experimental data (i.e. residual dipolar couplings and methyl‐methyl Nuclear Overhauser Effect signals) in the presented modeling pipeline depends mainly on size of a protein and complexity of its topology. In this work, we have provided tools for either post‐experiment processing of various kinds of NMR data or fast and low‐cost structural analysis in the still challenging field of new fold predictions. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011     

Protein fragment reconstruction using various modeling techniques

Recently developed reduced models of proteins with knowledge-based force fields have been applied to a specific case of comparative modeling. From twenty high resolution protein structures of various structural classes, significant fragments of their chains have been removed and treated as unknown. The remaining portions of the structures were treated as fixed - i.e., as templates with an exact alignment. Then, the missed fragments were reconstructed using several modeling tools. These included three reduced types of protein models: the lattice SICHO (Side Chain Only) model, the lattice CABS (Calpha + Cbeta + Side group) model and an off-lattice model similar to the CABS model and called REFINER. The obtained reduced models were compared with more standard comparative modeling tools such as MODELLER and the SWISS-MODEL server. The reduced model results are qualitatively better for the higher resolution lattice models, clearly suggesting that these are now mature, competitive and complementary (in the range of sparse alignments) to the classical tools of comparative modeling. Comparison between the various reduced models strongly suggests that the essential ingredient for the sucessful and accurate modeling of protein structures is not the representation of conformational space (lattice, off-lattice, all-atom) but, rather, the specificity of the force fields used and, perhaps, the sampling techniques employed. These conclusions are encouraging for the future application of the fast reduced models in comparative modeling on a genomic scale.     

Protein structure prediction by tempering spatial constraints

Summary The probability to predict correctly a protein structure can be enhanced through introduction of spatial constraints – either from NMR experiments or from homologous structures. However, the additional constraints lead often to new local energy minima and worse sampling efficiency in simulations. In this work, we present a new parallel tempering variant that alleviates the energy barriers resulting from spatial constraints and therefore yields to an enhanced sampling in structure prediction simulations.     

Protein structure prediction aided by geometrical and probabilistic constraints

Database-assisted ab initio protein structure prediction methods have exhibited considerable promise in the recent past, with several implementations being successful in community-wide experiments (CASP). We have employed combinatorial optimization techniques toward solving the protein structure prediction problem. A Monte Carlo minimization algorithm has been employed on a constrained search space to identify minimum energy configurations. The search space is constrained by using radius of gyration cutoffs, the loop backbone dihedral probability distributions, and various secondary structure packing conformations. Simulations have been carried out on several sequences and 1000 conformations have been initially generated. Of these, 50 best candidates have then been selected as probable conformations. The search for the optimum has been simplified by incorporating various geometrical constraints on secondary structural elements using distance restraint potential functions. The advantages of the reported methodology are its simplicity, and modifiability to include other geometric and probabilistic restraints.     

Protein Folding with a Reduced Model and Inaccurate Short‐Range Restraints

Summary: A reduced high‐coordination lattice protein model and the Replica Exchange Monte Carlo sampling were employed in de novo folding simulations of a set of representative small proteins. Three distinct situations were analyzed. In the first series of simulations, the folding was controlled purely by the generic force field of the model. In the second, a bias was introduced towards the theoretically predicted secondary structure. Finally, we superimposed soft restraints towards the native‐like local conformation of the backbone. The short‐range restraints used in these simulations are based on approximate values of ϕ and ψ dihedral angles, which may simulate restraints derived from inaccurate experimental measurements. Incorporating such data into the reduced model required developing a procedure, which transforms the ϕ and ψ coordinates into coordinates of the protein alpha carbon trace. It has been shown that such limited data are sufficient for de novo determination of three‐dimensional structures of small and topologically not too complex proteins.

Protein folding based on secondary structure prediction and simulated torsion angles data.     

Time-averaged predictions of folded and misfolded peptides using a reduced physicochemical model

Energy-based methods for calculating time-averaged peptide structures are important for rational peptide design, for defining local structure propensities in large protein chains, and for exploring the sequence determinants of amyloid formation. High-end methods are currently too slow to be practicable, and will remain so for the foreseeable future. The challenge is to create a method that runs quickly on limited computer resources and emulates reality sufficiently well. We have developed a simplified off-lattice protein model, incorporating semi-empirical physicochemical potentials, and combined it with an efficient Monte Carlo method for calculating time-averaged peptide structures. Reasonably accurate predictions are found for a set of small alpha-helical and beta-hairpin peptides, and we demonstrate a potential application in measuring local structure propensities in protein chains. Time-averaged structures have also been calculated for a set of small peptides known to form beta-amyloid fibrils. The simulations were of three interacting peptides, and in each case the time-averaged structure describes a three-stranded beta-sheet. The performance of our method in measuring the propensities of small peptides to self-associate into possible prefibrillar species compares favorably with more detailed and CPU-intensive approaches.     

De novo and inverse folding predictions of protein structure and dynamics

Summary In the last two years, the use of simplified models has facilitated major progress in the globular protein folding problem, viz., the prediction of the three-dimensional (3D) structure of a globular protein from its amino acid sequence. A number of groups have addressed the inverse folding problem where one examines the compatibility of a given sequence with a given (and already determined) structure. A comparison of extant inverse protein-folding algorithms is presented, and methodologies for identifying sequences likely to adopt identical folding topologies, even when they lack sequence homology, are described. Extension to produce structural templates or fingerprints from idealized structures is discussed, and for eight-membered β-barrel proteins, it is shown that idealized fingerprints constructed from simple topology diagrams can correctly identify sequences having the appropriate topology. Furthermore, this inverse folding algorithm is generalized to predict elements of supersecondary structure including β-hairpins, helical hairpins and α/β/α fragments. Then, we describe a very high coordination number lattice model that can predict the 3D structure of a number of globular proteins de novo; i.e. using just the amino acid sequence. Applications to sequences designed by DeGrado and co-workers [Biophys. J., 61 (1992) A265] predict folding intermediates, native states and relative stabilities in accord with experiment. The methodology has also been applied to the four-helix bundle designed by Richardson and co-workers [Science, 249 (1990) 884] and a redesigned monomeric version of a naturally occurring four-helix dimer, rop. Based on comparison to the rop dimer, the simulations predict conformations with rms values of 3–4 ? from native. Furthermore, the de novo algorithms can asses the stability of the folds predicted from the inverse algorithm, while the inverse folding algorithms can assess the quality of the de novo models. Thus, the synergism of the de novo and inverse folding algorthhm approaches provides a set of complementary tools that will facilitate further progress on the protein-folding problem.     

Protein structure prediction from inaccurate and sparse NMR data using an enhanced genetic algorithm

Nuclear Magnetic Resonance Spectroscopy (most commonly known as NMR Spectroscopy) is used to generate approximate and partial distances between pairs of atoms of the native structure of a protein. To predict protein structure from these partial distances by solving the Euclidean distance geometry problem from the partial distances obtained from NMR Spectroscopy, we can predict three-dimensional (3D) structure of a protein. In this paper, a new genetic algorithm is proposed to efficiently address the Euclidean distance geometry problem towards building 3D structure of a given protein applying NMR's sparse data. Our genetic algorithm uses (i) a greedy mutation and crossover operator to intensify the search; (ii) a twin removal technique for diversification in the population; (iii) a random restart method to recover from stagnation; and (iv) a compaction factor to reduce the search space. Reducing the search space drastically, our approach improves the quality of the search. We tested our algorithms on a set of standard benchmarks. Experimentally, we show that our enhanced genetic algorithms significantly outperforms the traditional genetic algorithms and a previously proposed state-of-the-art method. Our method is capable of producing structures that are very close to the native structures and hence, the experimental biologists could adopt it to determine more accurate protein structures from NMR data.     

蛋白质全新设计：八残基序列形成发夹结构的圆二色谱

β-发夹是天然蛋白质中丰富的二级结构单元之一，在蛋白折叠和功能方面扮演着重要角色.文章报导了二条多肽序列（LTVd-PGLTV，n7和 LTVGDDTV， n5）的设计、合成和园二色谱研究结果.结果显示，n5在198 nm附近呈现负峰，表现为非规整结构特征；相反，n7表现为典型的发夹结构特征，在218 nm附近呈负峰,196 nm附近呈正峰，为β-转角与β-折叠的共同贡献.初步研究表明，β-转角、序列关系和氨基酸形成β-折叠结构倾向性是β-发夹结构形成和稳定的决定性因素.     

Incorporation of FT-IR spectral data in a computer-assisted prediction of globular protein structure

Currently, much effort is being directed to the determination of the three-dimensional structure of proteins. Two classes of research are of interest; spectrometric techniques which include Fourier transform infrared (FT-IR) spectrometry, and non-spectrometric prediction schemes. The spectra obtained using FT-IR spectrometry, are analyzed to determine the percentages of alpha-helices, beta-pleated sheets, and non-structured coils in a protein. Unfortunately, FT-IR, as well as other spectrometric techniques, cannot be used to determine the exact secondary structure of a protein reliably. Non-spectrometric prediction methods yield information on the exact secondary structure, but are not always accurate. Most prediction methods relate the primary amino acid sequence to the secondary structure of a protein, allowing sequential secondary structure information for the protein examined to be obtained. The goal of this research is to incorporate FT-IR with a prediction method, resulting in an improvement in the accuracy of the prediction.     

Protein engineering ofDe novo protein with predesigned structure and activity

Thede novo protein albebetin has been engineered (J. Mol. Biol. 1992,225, 927–931) to form a predesigned tertiary fold that has not yet been observed in natural proteins. Analysis of albebetin expressed in a cell-free system and inEscherichia coli revealed its compactness, relative stability, and the secondary structure close to the predesigned one. The blast-transforming biological activity of human interferon was grafted to albebetin by attachment of an eight amino acid interferon fragment to the N-terminus of albebetin next to its first methionine residue. The chimeric protein was expressed in a wheat germ cell-free translation system and tested for its structural properties, receptor binding, and biological activity. According to the tests, albebetin incorporating the active interferon fragment has a compact and relatively stable structure, and binds the murine thymocyte recep or effectively. It activates the blast transformation reaction of thymo yte cells even more efficiently than human interferon at low concentrations.     

Quantum chemical electron impact mass spectrum prediction for de novo structure elucidation: Assessment against experimental reference data and comparison to competitive fragmentation modeling

We investigate the success of the quantum chemical electron impact mass spectrum (QCEIMS) method in predicting the electron impact mass spectra of a diverse test set of 61 small molecules selected to be representative of common fragmentations and reactions in electron impact mass spectra. Comparison with experimental spectra is performed using the standard matching algorithms, and the relative ranking position of the actual molecule matching the spectra within the NIST‐11 library is examined. We find that the correct spectrum is ranked in the top two matches from structural isomers in more than 50% of the cases. QCEIMS, thus, reproduces the distribution of peaks sufficiently well to identify the compounds, with the RMSD and mean absolute difference between appropriately normalized predicted and experimental spectra being at most 9% and 3% respectively, even though the most intense peaks are often qualitatively poorly reproduced. We also compare the QCEIMS method to competitive fragmentation modeling for electron ionization, a training‐based mass spectrum prediction method, and remarkably we find the QCEIMS performs equivalently or better. We conclude that QCEIMS will be very useful for those who wish to identify new compounds which are not well represented in the mass spectral databases.     

Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets

De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from‐scratch construction of molecules is not limited to compounds in pre‐existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK's advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known X‐ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ∼13M drug‐like compounds (generic libraries), and (3) application to a challenging protein‐protein interface on the viral drug target HIVgp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets. © 2017 Wiley Periodicals, Inc.     

Neural Networks Predict Protein Folding and Structure: Artificial Intelligence Faces Biomolecular Complexity

Abstract

In the genomic era DNA sequencing is increasing our knowledge of the molecular structure of genetic codes from bacteria to man at a hyperbolic rate. Billions of nucleotides and millions of aminoacids are already filling the electronic files of the data bases presently available, which contain a tremendous amount of information on the most biologically relevant macromolecules, such as DNA. RNA and proteins. The most urgent problem originates from the need to single out the relevant information amidst a wealth of general features. Intelligent tools are therefore needed to optimise the search. Data mining for sequence analysis in biotechnology has been substantially aided by the development of new powerful methods borrowed from the machine learning approach. In this paper we discuss the application of artificial feedforward neural networks to deal with some fundamental problems tied with the folding process and the structure-function relationship in proteins.     

PHAISTOS: A framework for Markov chain Monte Carlo simulation and inference of protein structure

We present a new software framework for Markov chain Monte Carlo sampling for simulation, prediction, and inference of protein structure. The software package contains implementations of recent advances in Monte Carlo methodology, such as efficient local updates and sampling from probabilistic models of local protein structure. These models form a probabilistic alternative to the widely used fragment and rotamer libraries. Combined with an easily extendible software architecture, this makes PHAISTOS well suited for Bayesian inference of protein structure from sequence and/or experimental data. Currently, two force‐fields are available within the framework: PROFASI and OPLS‐AA/L, the latter including the generalized Born surface area solvent model. A flexible command‐line and configuration‐file interface allows users quickly to set up simulations with the desired configuration. PHAISTOS is released under the GNU General Public License v3.0. Source code and documentation are freely available from http://phaistos.sourceforge.net . The software is implemented in C++ and has been tested on Linux and OSX platforms. © 2013 Wiley Periodicals, Inc.     

Finding the needle in a haystack: educing native folds from ambiguous ab initio protein structure predictions

Current ab initio structure‐prediction methods are sometimes able to generate families of folds, one of which is native, but are unable to single out the native one due to imperfections in the folding potentials and an inability to conduct thorough explorations of the conformational space. To address this issue, here we describe a method for the detection of statistically significant folds from a pool of predicted structures. Our approach consists of clustering and averaging the structures into representative fold families. Using a metric derived from the root‐mean‐square distance (RMSD) that is less sensitive to protein size, we determine whether the simulated structures are clustered in relation to a group of random structures. The clustering method searches for cluster centers and iteratively calculates the clusters and their respective centroids. The centroid interresidue distances are adjusted by minimizing a potential constructed from the corresponding average distances of the cluster structures. Application of this method to selected proteins shows that it can detect the best fold family that is closest to native, along with several other misfolded families. We also describe a method to obtain substructures. This is useful when the folding simulation fails to give a total topology prediction but produces common subelements among the structures. We have created a web server that clusters user submitted structures, which can be found at http://bioinformatics.danforthcenter.org/services/scar. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 339–353, 2001     

RMC_POT: A computer code for reverse monte carlo modeling the structure of disordered systems containing molecules of arbitrary complexity

An approach has been devised and tested for preserving the molecular dynamics molecular geometry taking into account energetic considerations during Reverse Monte Carlo (RMC) modeling. Instead of the commonly used fixed neighbor constraints, where molecules are held together by constraining distance ranges available for the specified atom pairs, here molecules are kept together via bond, angle, and dihedral potential energies. The scaled total potential energy contributes to the measure of the goodness‐of‐fit, thus, the atoms can be prevented from drifting apart. In some of the calculations (Lennard‐Jones and Coulombic) nonbonding potentials were also applied. The algorithm was successfully tested for the X‐ray structure factor‐based structure study of liquid dimethyl trisulfide, for which material now significantly more sensible results have been obtained than during previous attempts via any earlier version of RMC modeling. It is envisaged that structural modeling of a large class of materials, primarily liquids and amorphous solids containing molecules of up to about 100 atoms, will make use of the new code in the near future. © 2012 Wiley Periodicals, Inc.