Exploratory studies of ab initio protein structure prediction: multiple copy simulated annealing, AMBER energy functions, and a generalized born/solvent accessibility solvation model

Multilayered Computational Framework for Designing Peptide Inhibitors of HVEM-LIGHT Interaction

The herpesvirus entry mediator (HVEM) and its ligand LIGHT play crucial roles in immune system regulation, including T-cell proliferation, B-cell differentiation, and immunoglobulin secretion. However, excessive T-cell activation can lead to chronic inflammation and autoimmune diseases. Thus, inhibiting the HVEM-LIGHT interaction emerges as a promising therapeutic strategy for these conditions and in preventing adverse reactions in organ transplantation. This study focused on designing peptide inhibitors, targeting the HVEM-LIGHT interaction, using molecular dynamics (MD) simulations of 65 peptides derived from HVEM. These peptides varied in length and disulfide-bond configurations, crucial for their interaction with the LIGHT trimer. By simulating 31 HVEM domain variants, including the full-length protein, we assessed conformational changes upon LIGHT binding to understand the influence of HVEM segments and disulfide bonds on the binding mechanism. Employing multitrajectory microsecond-scale, all-atom MD simulations and molecular mechanics with generalized Born and surface area (MM-GBSA) binding energy estimation, we identified promising CRD2 domain variants with high LIGHT affinity. Notably, point mutations in these variants led to a peptide with a single disulfide bond (C58-C73) and a K54E substitution, exhibiting the highest binding affinity. The importance of the CRD2 domain and Cys58-Cys73 disulfide bond for interrupting HVEM-LIGHT interaction was further supported by analyzing truncated CRD2 variants, demonstrating similar binding strengths and mechanisms. Further investigations into the binding mechanism utilized steered MD simulations at various pulling speeds and umbrella sampling to estimate the energy profile of HVEM-based inhibitors with LIGHT. These comprehensive analyses revealed key interactions and different binding mechanisms, highlighting the increased binding affinity of selected peptide variants. Experimental circular dichroism techniques confirmed the structural properties of these variants. This study not only advances our understanding of the molecular basis of HVEM-LIGHT interactions but also provides a foundation for developing novel therapeutic strategies for immune-related disorders. Furthermore, it sets a gold standard for peptide inhibitor design in drug development due to its systematic approach.

Hydrophobicity-Based Force Field In Enzymes

The biocatalysis process takes place with the participation of enzymes, which, depending on the reaction carried out, require, apart from the appropriate arrangement of catalytic residues, an appropriate external force field. It is generated by the protein body. The relatively small size of the part directly involved in the process itself is supported by the presence of an often complex structure of the protein body, the purpose of which is to provide an appropriate local force field, eliminating the influence of water. Very often, the large size of the enzyme is an expression of the complex form of this field. In this paper, a comparative analysis of arbitrarily selected enzymes, representatives of different enzyme classes, was carried out, focusing on the measurement of the diversity of the force field provided by a given protein. This analysis was based on the fuzzy oil drop model (FOD) and its modified version (FOD-M), which takes into account the participation of nonaqueous external factors in shaping the structure and thus the force field within the protein. The degree and type of ordering of the hydrophobicity distribution in the protein molecule is the result of the influence of the environment but also the supplier of the local environment for a given process, including the catalysis process in particular. Determining the share of a nonaqueous environment is important due to the ubiquity of polar water, whose participation in processes with high specificity requires control. It can be assumed that some enzymes in their composition have a permanently built-in part, the role of which is reduced to that of a permanent chaperone. It provides a specific external force field needed for the process. The proposed model, generalized to other types of proteins, may also provide a form of recording the environment model for the simulation of the in silico protein folding process, taking into account the impact of its differentiation.

H-Bonds in Crambin: Coherence in an α-Helix

We applied coherence analysis-used by engineers to identify linear interactions in stochastic systems-to molecular dynamics simulations of crambin, a thionin storage protein found in Abyssinian cabbage. A key advantage of coherence over other analyses is that it is robust, independent of the properties, or even the existence of probability distributions often relied on in statistical mechanics. For frequencies between 0.391 and 5.08 GHz (corresponding reciprocally to times of 2.56 and 0.197 ns), the displacements of oxygen and nitrogen atoms across α-helix H-bonds are strongly correlated, with a coherence greater than 0.9; the secondary structure causes the H-bonds to effectively act as a spring. Similar coherence behavior is observed for covalent bonds and other noncovalent interactions including H-bonds in β-sheets and salt bridges. In contrast, arbitrary pairs of atoms that are physically distant have uncorrelated motions and negligible coherence. These results suggest that coherence may be used to objectively identify atomic interactions without subjective thresholds such as H-bond lengths angles and angles. Strong coherence is also observed between the average position of adjacent leaves (groups of atoms) in an α-helix, suggesting that the harmonic analysis of classical molecular dynamics can successfully describe the propagation of allosteric interactions through the structure.

Effects of Site-Directed Mutagenesis of Cysteine on the Structure of Sip Proteins

Bacillus thuringiensis, a gram-positive bacteria, has three insecticidal proteins: Vip (vegetative insecticidal protein), Cry (crystal), and Sip (secreted insecticidal protein). Of the three, Sip proteins have insecticidal activity against larvae of Coleoptera. However, the Sip1Aa protein has little solubility in the supernatant because of inclusion bodies. This makes it more difficult to study, and thus research on Sip proteins is limited, which hinders the study of their mechanistic functions and insecticidal mechanisms. This highlights the importance of further investigation of the Sip1Aa protein. Disulfide bonds play an important role in the stability and function of proteins. Here, we successfully constructed mutant proteins with high insecticidal activity. The tertiary structure of the Sip1Aa protein was analyzed with homologous modeling and bioinformatics to predict the conserved domain of the protein. Cysteine was used to replace amino acids via site-directed mutagenesis. We successfully constructed Sip149-251, Sip153-248, Sip158-243, and Sip178-314 mutant proteins with higher solubility than Sip1Aa. Sip153-248 and Sip158-243 were the most stable compared to Sip1Aa, followed by Sip149-251 and Sip178-314. The insecticidal activity of Sip153-248 (Sip158-243) was 2.76 (2.26) times higher than that of Sip1Aa. The insecticidal activity of Sip149-251 and Sip178-314 did not differ significantly from that of Sip1Aa. Basic structural properties, physicochemical properties, and the spatial structure of the mutation site of Sip1Aa and the mutant proteins were analyzed. These results provide a molecular basis for using Sip1Aa to control Coleopteran insects and contribute to the study of the Sip1Aa insecticidal mechanism.

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

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.

OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION

Physics-based computational approaches to predicting the structure of macromolecules such as proteins are gaining increased use, but there are remaining challenges. In the current work, it is demonstrated that in energy-based prediction methods, the degree of optimization of the sampled structures can influence the prediction results. In particular, discrepancies in the degree of local sampling can bias the predictions in favor of the oversampled structures by shifting the local probability distributions of the minimum sampled energies. In simple systems, it is shown that the magnitude of the errors can be calculated from the energy surface, and for certain model systems, derived analytically. Further, it is shown that for energy wells whose forms differ only by a randomly assigned energy shift, the optimal accuracy of prediction is achieved when the sampling around each structure is equal. Energy correction terms can be used in cases of unequal sampling to reproduce the total probabilities that would occur under equal sampling, but optimal corrections only partially restore the prediction accuracy lost to unequal sampling. For multiwell systems, the determination of the correction terms is a multibody problem; it is shown that the involved cross-correlation multiple integrals can be reduced to simpler integrals. The possible implications of the current analysis for macromolecular structure prediction are discussed.

MISS-Prot: web server for self/non-self discrimination of protein residue networks in parasites; theory and experiments in Fasciola peptides and Anisakis allergens

Infections caused by human parasites (HPs) affect the poorest 500 million people worldwide but chemotherapy has become expensive, toxic, and/or less effective due to drug resistance. On the other hand, many 3D structures in Protein Data Bank (PDB) remain without function annotation. We need theoretical models to quickly predict biologically relevant Parasite Self Proteins (PSP), which are expressed differentially in a given parasite and are dissimilar to proteins expressed in other parasites and have a high probability to become new vaccines (unique sequence) or drug targets (unique 3D structure). We present herein a model for PSPs in eight different HPs (Ascaris, Entamoeba, Fasciola, Giardia, Leishmania, Plasmodium, Trypanosoma, and Toxoplasma) with 90% accuracy for 15 341 training and validation cases. The model combines protein residue networks, Markov Chain Models (MCM) and Artificial Neural Networks (ANN). The input parameters are the spectral moments of the Markov transition matrix for electrostatic interactions associated with the protein residue complex network calculated with the MARCH-INSIDE software. We implemented this model in a new web-server called MISS-Prot (MARCH-INSIDE Scores for Self-Proteins). MISS-Prot was programmed using PHP/HTML/Python and MARCH-INSIDE routines and is freely available at: . This server is easy to use by non-experts in Bioinformatics who can carry out automatic online upload and prediction with 3D structures deposited at PDB (mode 1). We can also study outcomes of Peptide Mass Fingerprinting (PMFs) and MS/MS for query proteins with unknown 3D structures (mode 2). We illustrated the use of MISS-Prot in experimental and/or theoretical studies of peptides from Fasciola hepatica cathepsin proteases or present on 10 Anisakis simplex allergens (Ani s 1 to Ani s 10). In doing so, we combined electrophoresis (1DE), MALDI-TOF Mass Spectroscopy, and MASCOT to seek sequences, Molecular Mechanics + Molecular Dynamics (MM/MD) to generate 3D structures and MISS-Prot to predict PSP scores. MISS-Prot also allows the prediction of PSP proteins in 16 additional species including parasite hosts, fungi pathogens, disease transmission vectors, and biotechnologically relevant organisms.

Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica

There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets like proteins. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately, most QSAR models predict activity against only one protein. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 32:32-15-1:1. This MLP classifies correctly 623 out of 678 DTPs (Sensitivity = 91.89%) and 2995 out of 3234 nDTPs (Specificity = 92.61%), corresponding to training Accuracy = 92.48%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 313 out of 338 DTPs (Sensitivity = 92.60%) and 1411 out of 1534 nDTP (Specificity = 91.98%) in validation series, corresponding to total Accuracy = 92.09% for validation series (Predictability). This model favorably compares with other LDA and ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. These mt-QSARs offer also a good opportunity to construct drug-protein Complex Networks (CNs) that can be used to explore large and complex drug-protein receptors databases. Finally, we illustrated two practical uses of this model with two different experiments. In experiment 1, we report prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of 10 rasagiline derivatives promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, SEC and 1DE sample preparation, MALDI-TOF MS and MS/MS analysis, MASCOT search, MM/MD 3D structure modeling, and QSAR prediction for different peptides of hemoglobin found in the proteome of the human parasite Fasciola hepatica; which is promising for anti-parasite drug targets discovery.

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward.

Ab initio protein structure prediction using pathway models

Ab initio prediction is the challenging attempt to predict protein structures based only on sequence information and without using templates. It is often divided into two distinct sub-problems: (a) the scoring function that can distinguish native, or native-like structures, from non-native ones; and (b) the method of searching the conformational space. Currently, there is no reliable scoring function that can always drive a search to the native fold, and there is no general search method that can guarantee a significant sampling of near-natives. Pathway models combine the scoring function and the search. In this short review, we explore some of the ways pathway models are used in folding, in published works since 2001, and present a new pathway model, HMMSTR-CM, that uses a fragment library and a set of nucleation/propagation-based rules. The new method was used for ab initio predictions as part of CASP5. This work was presented at the Winter School in Bioinformatics, Bologna, Italy, 10–14 February 2003.

Prediction of enzyme classes from 3D structure: a general model and examples of experimental-theoretic scoring of peptide mass fingerprints of Leishmania proteins

The number of protein and peptide structures included in Protein Data Bank (PDB) and Gen Bank without functional annotation has increased. Consequently, there is a high demand for theoretical models to predict these functions. Here, we trained and validated, with an external set, a Markov Chain Model (MCM) that classifies proteins by their possible mechanism of action according to Enzyme Classification (EC) number. The methodology proposed is essentially new, and enables prediction of all EC classes with a single equation without the need for an equation for each class or nonlinear models with multiple outputs. In addition, the model may be used to predict whether one peptide presents a positive or negative contribution of the activity of the same EC class. The model predicts the first EC number for 106 out of 151 (70.2%) oxidoreductases, 178/178 (100%) transferases, 223/223 (100%) hydrolases, 64/85 (75.3%) lyases, 74/74 (100%) isomerases, and 100/100 (100%) ligases, as well as 745/811 (91.9%) nonenzymes. It is important to underline that this method may help us predict new enzyme proteins or select peptide candidates that improve enzyme activity, which may be of interest for the prediction of new drugs or drug targets. To illustrate the model's application, we report the 2D-Electrophoresis (2DE) isolation from Leishmania infantum as well as MADLI TOF Mass Spectra characterization and theoretical study of the Peptide Mass Fingerprints (PMFs) of a new protein sequence. The theoretical study focused on MASCOT, BLAST alignment, and alignment-free QSAR prediction of the contribution of 29 peptides found in the PMF of the new protein to specific enzyme action. This combined strategy may be used to identify and predict peptides of prokaryote and eukaryote parasites and their hosts as well as other superior organisms, which may be of interest in drug development or target identification.

A coarse-grained protein force field for folding and structure prediction

We have revisited the protein coarse-grained optimized potential for efficient structure prediction (OPEP). The training and validation sets consist of 13 and 16 protein targets. Because optimization depends on details of how the ensemble of decoys is sampled, trial conformations are generated by molecular dynamics, threading, greedy, and Monte Carlo simulations, or taken from publicly available databases. The OPEP parameters are varied by a genetic algorithm using a scoring function which requires that the native structure has the lowest energy, and the native-like structures have energy higher than the native structure but lower than the remote conformations. Overall, we find that OPEP correctly identifies 24 native or native-like states for 29 targets and has very similar capability to the all-atom discrete optimized protein energy model (DOPE), found recently to outperform five currently used energy models.

Study of peptide fingerprints of parasite proteins and drug-DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks

Since the advent of Molecular Dynamics (MD) in biopolymers science with the study by Karplus et al. on protein dynamics, MD has become the by foremost well established, computational technique to investigate structure and function of biomolecules and their respective complexes and interactions. The analysis of the MD trajectories (MDTs) remains, however, the greatest challenge and requires a great deal of insight, experience, and effort. Here, we introduce a new class of invariants for MDTs based on the spatial distribution of Mean-Energy values ξk (L) on a 2D Euclidean space representation of the MDTs. The procedure forces one MD trajectory to fold into a 2D Cartesian coordinates system using a step-by-step procedure driven by simple rules. The ξk (L) values are invariants of a Markov matrix (1 Π), which describes the probabilities of transition between two states in the new 2D space; which is associated to a graph representation of MDTs similar to the lattice networks (LNs) of DNA and protein sequences. We also introduce a new algorithm to perform phylogenetic analysis of peptides based on MDTs instead of the sequence of the polypeptide. In a first experiment, we illustrate this algorithm for 35 peptides present on the Peptide Mass Fingerprint (PMF) of a new protein of Leishmania infantum studied in this work. We report, by the first time, 2D Electrophoresis isolation, MALDI TOF Mass Spectroscopy characterization, and MASCOT search results for this PMF. In a second experiment, we construct the LNs for 422 MDTs obtained in DNA-Drug Docking simulations of the interaction of 57 anticancer furocoumarins with a DNA oligonucleotide. We calculated the respective ξk (L) values for all these LNs and used them as inputs to train a new classifier with Accuracy = 85.44% and 84.91% in training and validation respectively. The new model can be used as scoring function to guide DNA-Drug Docking studies in drug design of new coumarins for PUVA therapy. The new phylogenetics analysis algorithms encode information different from sequence similarity and may be used to analyze MDTs obtained in Docking or modeling experiments for any classes of biopolymers. The work opens new perspective on the analysis and applications of MD in polymer sciences.

TIGER2: an improved algorithm for temperature intervals with global exchange of replicas

An empirical sampling method for molecular simulation based on "temperature intervals with global exchange of replicas" (TIGER2) has been developed to reduce the high demand for computational resources and the low computational efficiency of the conventional replica-exchange molecular dynamics (REMD) method. This new method overcomes the limitation of its previous version, called TIGER, which requires the assumption of constant heat capacity during quenching of replicas from elevated temperatures to the baseline temperature. The robustness of the TIGER2 method is examined by comparing it against a Metropolis Monte Carlo simulation for sampling the conformational distribution of a single butane molecule in vacuum, a REMD simulation for sampling the behavior of alanine dipeptide in explicit solvent, and REMD simulations for sampling the folding behavior of two peptides, (AAQAA)(3) and chignolin, in implicit solvent. The agreement between the results from these conventional sampling methods and the TIGER2 simulations indicates that the TIGER2 algorithm is able to closely approximate a Boltzmann-weighted ensemble of states for these systems but without the limiting assumptions that were required for the original TIGER algorithm. TIGER2 is an efficient replica-exchange sampling method that enables the number of replicas that are used for a replica-exchange simulation to be substantially reduced compared to the conventional REMD method.

QSAR for RNases and theoretic-experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein

The toxicity and low success of current treatments for Leishmaniosis determines the search of new peptide drugs and/or molecular targets in Leishmania pathogen species (L. infantum and L. major). For example, Ribonucleases (RNases) are enzymes relevant to several biologic processes; then, theoretical and experimental study of the molecular diversity of Peptide Mass Fingerprints (PMFs) of RNases is useful for drug design. This study introduces a methodology that combines QSAR models, 2D-Electrophoresis (2D-E), MALDI-TOF Mass Spectroscopy (MS), BLAST alignment, and Molecular Dynamics (MD) to explore PMFs of RNases. We illustrate this approach by investigating for the first time the PMFs of a new protein of L. infantum. Here we report and compare new versus old predictive models for RNases based on Topological Indices (TIs) of Markov Pseudo-Folding Lattices. These group of indices called Pseudo-folding Lattice 2D-TIs include: Spectral moments pi ( k )(x,y), Mean Electrostatic potentials xi ( k )(x,y), and Entropy measures theta ( k )(x,y). The accuracy of the models (training/cross-validation) was as follows: xi ( k )(x,y)-model (96.0%/91.7%)>pi ( k )(x,y)-model (84.7/83.3) > theta ( k )(x,y)-model (66.0/66.7). We also carried out a 2D-E analysis of biological samples of L. infantum promastigotes focusing on a 2D-E gel spot of one unknown protein with M<20, 100 and pI <7. MASCOT search identified 20 proteins with Mowse score >30, but not one >52 (threshold value), the higher value of 42 was for a probable DNA-directed RNA polymerase. However, we determined experimentally the sequence of more than 140 peptides. We used QSAR models to predict RNase scores for these peptides and BLAST alignment to confirm some results. We also calculated 3D-folding TIs based on MD experiments and compared 2D versus 3D-TIs on molecular phylogenetic analysis of the molecular diversity of these peptides. This combined strategy may be of interest in drug development or target identification.

Protein structure search and local structure characterization

Background

Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.

Results

We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at .

Conclusion

The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.

Insights into subunit interactions in the heterotetrameric structure of potato ADP-glucose pyrophosphorylase

ADP-glucose pyrophosphorylase, a key allosteric enzyme involved in higher plant starch biosynthesis, is composed of pairs of large (LS) and small subunits (SS). Current evidence indicates that the two subunit types play distinct roles in enzyme function. The LS is involved in mainly allosteric regulation through its interaction with the catalytic SS. Recently the crystal structure of the SS homotetramer has been solved, but no crystal structure of the native heterotetrameric enzyme is currently available. In this study, we first modeled the three-dimensional structure of the LS to construct the heterotetrameric enzyme. Because the enzyme has a 2-fold symmetry, six different dimeric (either up-down or side-by-side) interactions were possible. Molecular dynamics simulations were carried out for each of these possible dimers. Trajectories obtained from molecular dynamics simulations of each dimer were then analyzed by the molecular mechanics/Poisson-Boltzmann surface area method to identify the most favorable dimers, one for up-down and the other for side-by-side. Computational results combined with site directed mutagenesis and yeast two hybrid experiments suggested that the most favorable heterotetramer is formed by LS-SS (side-by-side), and LS-SS (up-down). We further determined the order of assembly during the heterotetrameric structure formation. First, side-by-side LS-SS dimers form followed by the up-down tetramerization based on the relative binding free energies.

Prediction of Protein Loop Conformations using the AGBNP Implicit Solvent Model and Torsion Angle Sampling

The OPLS-AA all-atom force field and the Analytical Generalized Born plus Non-Polar (AGBNP) implicit solvent model, in conjunction with torsion angle conformational search protocols based on the Protein Local Optimization Program (PLOP), are shown to be effective in predicting the native conformations of 57 9-residue and 35 13-residue loops of a diverse series of proteins with low sequence identity. The novel nonpolar solvation free energy estimator implemented in AGBNP augmented by correction terms aimed at reducing the occurrence of ion pairing are important to achieve the best prediction accuracy. Extended versions of the previously developed PLOP-based conformational search schemes based on calculations in the crystal environment are reported that are suitable for application to loop homology modeling without the crystal environment. Our results suggest that in general the loop backbone conformation is not strongly influenced by crystal packing. The application of the temperature Replica Exchange Molecular Dynamics (T-REMD) sampling method for a few examples where PLOP sampling is insufficient are also reported. The results reported indicate that the OPLS-AA/AGBNP effective potential is suitable for high-resolution modeling of proteins in the final stages of homology modeling and/or protein crystallographic refinement.

Optimum folding pathways for growing protein chains

The folding of a protein is studied as it grows residue by residue from the N-terminus and enters an environment that stabilizes the folded state. This mode of folding of a growing chain is different from refolding where the full chain folds from a disordered initial configuration to the native state. We propose a sequential dynamic optimization method that computes the evolution of optimum folding pathways as amino acid residues are added to the peptide chain one by one. The dynamic optimization formulation is deterministic and uses Newton's equations of motion and a Go-type potential that establishes the native contacts and excluded volume effects. The method predicts the optimal energy-minimizing path among all the alternative feasible pathways. As two examples, the folding of the chicken villin headpiece, a 36-residue protein, and chymotrypsin inhibitor 2 (CI2), a 64-residue protein, are studied. Results on the villin headpiece show significant differences from the refolding of the same chain studied previously. Results on CI2 mostly agree with the results of refolding experiments and computational work.

Overcoming entropic barrier with coupled sampling at dual resolutions

An enhanced sampling method is proposed for ab initio protein folding simulations. The new method couples a high-resolution model for accuracy and a low-resolution model for efficiency. It aims to overcome the entropic barrier found in the exponentially large protein conformational space when a high-resolution model, such as an all-atom molecular mechanics force field, is used. The proposed method is designed to satisfy the detailed balance condition so that the Boltzmann distribution can be generated in all sampling trajectories in both high and low resolutions. The method was tested on model analytical energy functions and ab initio folding simulations of a beta-hairpin peptide. It was found to be more efficient than replica-exchange method that is used as its building block. Analysis with the analytical energy functions shows that the number of energy calculations required to find global minima and to converge mean potential energies is much fewer with the new method. Ergodic measure shows that the new method explores the conformational space more rapidly. We also studied imperfect low-resolution energy models and found that the introduction of errors in low-resolution models does decrease its sampling efficiency. However, a reasonable increase in efficiency is still observed when the global minima of the low-resolution models are in the vicinity of the global minimum basin of the high-resolution model. Finally, our ab initio folding simulation of the tested peptide shows that the new method is able to fold the peptide in a very short simulation time. The structural distribution generated by the new method at the equilibrium portion of the trajectory resembles that in the equilibrium simulation starting from the crystal structure.

Global optimization and folding pathways of selected alpha-helical proteins

The results of basin-hopping global optimization simulations are presented for four small, alpha-helical proteins described by a coarse-grained potential. A step-taking scheme that incorporates the local conformational preferences extracted from a large number of high-resolution protein structures is compared with an unbiased scheme. In addition, the discrete path sampling method is used to investigate the folding of one of the proteins, namely, the villin headpiece subdomain. Folding times from kinetic Monte Carlo simulations and iterative calculations based on a Markovian first-step analysis for the resulting stationary-point database are in good mutual agreement, but differ significantly from the experimental values, probably because the native state is not the global free energy minimum for the potential employed.

Are solvation free energies of homogeneous helical peptides additive?

We investigated the additivity of the solvation free energy of amino acids in homogeneous helices of different length in water and in chloroform. Solvation free energies were computed by multiconfiguration thermodynamic integration involving extended molecular dynamics simulations and by applying the generalized-born surface area solvation model to static helix geometries. The investigation focused on homogeneous peptides composed of uncharged amino acids, where the backbone atoms are kept fixed in an ideal helical conformation. We found nonlinearity especially for short peptides, which does not allow a simple treatment of the interaction of amino acids with their surroundings. For homogeneous peptides longer than five residues, the results from both methods are in quite good agreement and solvation energies are to a good extent additive.

Binding of antifusion peptides with HIVgp41 from molecular dynamics simulations: quantitative correlation with experiment

Peptides based on C-terminal regions of the human immunodeficiency virus (HIV) viral protein gp41 represent an important new class of antiviral therapeutics called peptide fusion inhibitors. In this study, computational methods were used to model the binding of six peptides that contain residues that pack into a conserved hydrophobic pocket on HIVgp41, an attractive target site for the development of small molecule inhibitors. Free energies of binding were computed using molecular mechanics Generalized Born surface area (MM-GBSA) methods from molecular dynamics (MD) simulations, which employed either explicit (TIP3P) or continuum Generalized Born (GB) water models and strong correlations between experimental and computational affinities were obtained in both cases. Energy decomposition of the TIP3P-MD results (r2 = 0.75) reveals that variation in experimental affinity is highly correlated with changes in intermolecular van der Waals energies (deltaE(vdw)) on both a local (residue-based, r2 = 0.94) and global (peptide-based, r2 = 0.84) scale. The results show that differential association of C-peptides with HIVgp41 is driven solely by changes within the conserved pocket supporting the hypothesis that this region is an important drug target site. Such strong agreement with experiment is notable given the large size of the ligands (34 amino-acids) relative to the small range of experimental affinities (2 kcal/mol) and demonstrates good sensitivity of this computational method for simulating peptide fusion inhibitors. Finally, inspection of simulation trajectories identified a highly populated pi-type hydrogen bond, which formed between Gln575 on the receptor and the aromatic ring of peptide ligand Phe631, which could have important implications for drug design.

Comparative study of generalized born models: Born radii and peptide folding

In this study, we have implemented four analytical generalized Born (GB) models and investigated their performance in conjunction with the GROMOS96 force field. The four models include that of Still and co-workers, the HCT model of Cramer, Truhlar, and co-workers, a modified form of the AGB model of Levy and co-workers, and the GBMV2 model of Brooks and co-workers. The models were coded independently and implemented in the GROMOS software package and in TINKER. They were compared in terms of their ability to reproduce the results of Poisson-Boltzmann (PB) calculations and in their performance in the ab initio peptide folding of two peptides, one that forms a beta-hairpin in solution and one that forms an alpha-helix. In agreement with previous work, the GBMV2 model is most successful in reproducing PB results while the other models tend to underestimate the effective Born radii of buried atoms. In contrast, stochastic dynamics simulations on the folding of the two peptides, the C-terminus beta-hairpin of the B1 domain of protein G and the alanine-based alpha-helical peptide 3K(I), suggest that the simpler GB models are more effective in sampling conformational space. Indeed, the Still model used in conjunction with the GROMOS96 force field is able to fold the hairpin peptide to a native-like structure without the benefit of enhanced sampling techniques. This is due in part to the properties of the united-atom GROMOS96 force field which appears to be more flexible, and hence to sample more efficiently, than force fields such as OPLSAA. Our results suggest a general strategy which involves using different combinations of force fields and solvent models in different applications, for example, using GROMOS96 and a simple GB model in sampling and OPLSAA and a more accurate GB model in refinement. The fact that various methods have been implemented in a unified way should facilitate the testing and subsequent use of different methods to evaluate conformational free energies in different applications. Our results also bear on some general issues involved in peptide folding and structure prediction which are addressed in the Discussion.

Identification of the PXW sequence as a structural gatekeeper of the headpiece C-terminal subdomain fold

The HeadPiece (HP) domain, present in several F-actin-binding multi-domain proteins, features a well-conserved, solvent-exposed PXWK motif in its C-terminal subdomain. The latter is an autonomously folding subunit comprised of three alpha-helices organised around a hydrophobic core, with the sequence motif preceding the last helix. We report the contributions of each conserved residue in the PXWK motif to human villin HP function and structure, as well as the structural implications of the naturally occurring Pro to Ala mutation in dematin HP. NMR shift perturbation mapping reveals that substitution of each residue by Ala induces only minor, local perturbations in the full villin HP structure. CD spectroscopic thermal analysis, however, shows that the Pro and Trp residues in the PXWK motif afford stabilising interactions. This indicates that, in addition to the residues in the hydrophobic core, the Trp-Pro stacking within the motif contributes to HP stability. This is reinforced by our data on isolated C-terminal HP subdomains where the Pro is also essential for structure formation, since the villin, but not the dematin, C-terminal subdomain is structured. Proper folding can be induced in the dematin C-terminal subdomain by exchanging the Ala for Pro. Conversely, the reverse substitution in the villin C-terminal subdomain leads to loss of structure. Thus, we demonstrate a crucial role for this proline residue in structural stability and folding potential of HP (sub)domains consistent with Pro-Trp stacking as a more general determinant of protein stability.

Balancing solvation and intramolecular interactions: toward a consistent generalized Born force field

The efficient and accurate characterization of solvent effects is a key element in the theoretical and computational study of biological problems. Implicit solvent models, particularly generalized Born (GB) continuum electrostatics, have emerged as an attractive tool to study the structure and dynamics of biomolecules in various environments. Despite recent advances in this methodology, there remain limitations in the parametrization of many of these models. In the present work, we demonstrate that it is possible to achieve a balanced implicit solvent force field by further optimizing the input atomic radii in combination with adjusting the protein backbone torsional energetics. This parameter optimization is guided by the potentials of mean force (PMFs) between amino acid polar groups, calculated from explicit solvent free energy simulations, and by conformational equilibria of short peptides, obtained from extensive folding and unfolding replica exchange molecular dynamics (REX-MD) simulations. Through the application of this protocol, the delicate balance between the competing solvation forces and intramolecular forces appears to be better captured, and correct conformational equilibria for a range of both helical and beta-hairpin peptides are obtained. The same optimized force field also successfully folds both beta-hairpin trpzip2 and mini-protein Trp-Cage, indicating that it is quite robust. Such a balanced, physics-based force field will be highly applicable to a range of biological problems including protein folding and protein structural dynamics.

ARTIST: An activated method in internal coordinate space for sampling protein energy landscapes

We present the first applications of an activated method in internal coordinate space for sampling all-atom protein conformations, the activation-relaxation technique for internal coordinate space trajectories (ARTIST). This method differs from all previous internal coordinate-based studies aimed at folding or refining protein structures in that conformational changes result from identifying and crossing well-defined saddle points connecting energy minima. Our simulations of four model proteins containing between 4 and 47 amino acids indicate that this method is efficient for exploring conformational space in both sparsely and densely packed environments, and offers new perspectives for applications ranging from computer-aided drug design to supramolecular assembly.

Improvement of a FRET-based indicator for cAMP by linker design and stabilization of donor-acceptor interaction

Förster resonance energy transfer (FRET) technology has been used to develop genetically encoded fluorescent indicators for a variety of intracellular molecular events. Often, however, the poor dynamic range of such reporters prevents detection of subtle but physiologically relevant signals. Here we present a strategy for improving FRET efficiency between donor and acceptor fluorophores in a green fluorescent protein (GFP)-based protein indicator for cAMP. Such indicator is based on protein kinase A (PKA) and was generated by fusion of CFP and YFP to the regulatory and catalytic subunits of PKA, respectively. Our approach to improve FRET efficiency was to perform molecular dynamic simulations and modelling studies of the linker peptide (L11) joining the CFP moiety and the regulatory subunit in order to define its structure and use this information to design an improved linker. We found that L11 contains the X-Y-P-Y-D motif, which adopts a turn-like conformation that is stiffly conserved along the simulation time. Based on this finding, we designed a new linker, L22 in which the YPY motif was doubled in order to generate a stiffer peptide and reduce the mobility of the chromophore within the protein complex, thus favouring CFP/YFP dipole-dipole interaction and improving FRET efficiency. Molecular dynamic simulations of L22 showed, unexpectedly, that the conformational behaviour of L22 was very loose. Based on the analysis of the three principal conformational states visited by L22 during the simulation time, we modified its sequence in order to increase its rigidity. The resulting linker L20 displayed lower flexibility and higher helical content than L22. When inserted in the cAMP indicator, L20 yielded a probe showing almost doubled FRET efficiency and a substantially improved dynamic range.

Recent advances in the development and application of implicit solvent models in biomolecule simulations

Advances have recently been made in the development of implicit solvent methodologies and their application to the modeling of biomolecules, particularly with regard to generalized Born approaches, dielectric screening function formulations and models based on solvent-accessible surface areas. Interesting new developments include more refined non-polar solvation energy estimators, and implicit methods for modeling low-dielectric and heterogeneous environments such as membrane systems. These have been successfully applied to molecular dynamics simulations, the scoring of protein conformations, and the calculation of binding affinities and folding free energy landscapes.

Conformational energy landscape of the acyl pocket loop in acetylcholinesterase: a Monte Carlo-generalized Born model study

The X-ray crystal structure of the reaction product of acetylcholinesterase (AChE) with the inhibitor diisopropylphosphorofluoridate (DFP) showed significant structural displacement in a loop segment of residues 287-290. To understand this conformational selection, a Monte Carlo (MC) simulation study was performed of the energy landscape for the loop segment. A computational strategy was applied by using a combined simulated annealing and room temperature Metropolis sampling approach with solvent polarization modeled by a generalized Born (GB) approximation. Results from thermal annealing reveal a landscape topology of broader basin opening and greater distribution of energies for the displaced loop conformation, while the ensemble average of conformations at 298 K favored a shift in populations toward the native by a free-energy difference in good agreement with the estimated experimental value. Residue motions along a reaction profile of loop conformational reorganization are proposed where Arg-289 is critical in determining electrostatic effects of solvent interaction versus Coulombic charging.

Peptide and protein folding and conformational equilibria: theoretical treatment of electrostatics and hydrogen bonding with implicit solvent models

Since biomolecules exist in aqueous and membrane environments, the accurate modeling of solvation, and hydrogen bonding interactions in particular, is essential for the exploration of structure and function in theoretical and computational studies. In this chapter, we focus on alternatives to explicit solvent models and discuss recent advances in generalized Born (GB) implicit solvent theories. We present a brief review of the successes and shortcomings of the application of these theories to biomolecular problems that are strongly linked to backbone H-bonding and electrostatics. This discussion naturally leads us to explore existing areas for improvement in current GB theories and our approach towards addressing a number of the key issues that remain in the refinement of these models. Specifically, the critical importance of balancing solvation forces and intramolecular forces in GB models is illustrated by examining the influence of backbone hydrogen bond strength and backbone dihedral energetics on conformational equilibria of small peptids.

Enhanced ab initio protein folding simulations in Poisson-Boltzmann molecular dynamics with self-guiding forces

We have investigated the sampling efficiency in molecular dynamics with the PB implicit solvent when self-guiding forces are added. Compared with a high-temperature dynamics simulation, the use of self-guiding forces in room-temperature dynamics is found to be rather efficient as measured by potential energy fluctuation, gyration radius fluctuation, backbone RMSD fluctuation, number of unique clusters, and distribution of low RMSD structures over simulation time. Based on the enhanced sampling method, we have performed ab initio folding simulations of two small proteins, betabetaalpha1 and villin headpiece. The preliminary data for the folding simulations is presented. It is found that betabetaalpha1 folding proceeds by initiation of the turn and the helix. The hydrophobic collapse seems to be lagging behind or at most concurrent with the formation of the helix. The hairpin stability is weaker than the helix in our simulations. Its role in the early folding events seems to be less important than the more stable helix. In contrast, villin headpiece folding proceeds first by hydrophobic collapse. The formation of helices is later than the collapse phase, different from the betabetaalpha1 folding.

Stability issues of covalently and noncovalently bonded peptide subunits

The present study focuses on important questions associated with modeling of peptide and protein stability. Computing at different levels of theory (RHF, B3LYP) for a representative ensemble of conformers of di- and tripeptides of alanine, we found that the Gibbs Free Energy values correlate significantly with the total electronic energy of the molecules (0.922 < or = R2). For noncovalently attached but interacting peptide subunits, such as [For-NH2]2 or [For-L-Ala-NH2]2, we have found, as expected, that the basis set superimposition error (BSSE) is large in magnitude for small basis set but significantly smaller when larger basis sets [e.g., B3LYP/6-311++G(d,p)] are used. Stability of the two hydrogen bonds of antiparallel beta-pleated sheets were quantitatively determined as a function of the molecular structure, S10 and S14, computed as 4.0 +/- 0.5 and 8.1 +/- 1.1 kcal/mol, respectively. Finally, a suitable thermoneutral isodesmic reaction was introduced to scale both covalently and noncovalently attached peptide units onto a common stability scale. We found that a suitable isodesmic reaction can result in the total electronic energy as well as the Gibbs free energy of a molecule, from its "noninteracting" fragments, as accurate as a few tenths of a kcal per mol. The latter observation seems to hold for peptides regardless of their length (1 < or = n < or = 8) or the level of theory applied.

Free energy surfaces of β-hairpin and α-helical peptides generated by replica exchange molecular dynamics with the AGBNP implicit solvent model

We have studied the potential of mean force of two peptides, one known to adopt a beta-hairpin and the other an alpha-helical conformation in solution. These peptides are, respectively, residues 41-56 of the C-terminus (GEWTYDDATKTFTVTE) of the B1 domain of protein G and the 13 residue C-peptide (KETAAAKFERQHM) of ribonuclease A. Extensive canonical ensemble sampling has been performed using a parallel replica exchange method. The effective potential employed in this work consists of the OPLS all-atom force field (OPLS-AA) and an analytical generalized Born (AGB) implicit solvent model including a novel nonpolar solvation free energy estimator (NP). An additional dielectric screening parameter has been incorporated into the AGBNP model. In the case of the beta-hairpin, the nonpolar solvation free energy estimator provides the necessary effective interactions for the collapse of the hydrophobic core (W43, Y45, F52, and V54), which the more commonly used surface-area-dependent nonpolar model does not provide. For both the beta-hairpin and the alpha-helix, increased dielectric screening reduces the stability of incorrectly formed salt bridges, which tend to disrupt the formation of the hairpin and helix, respectively. The fraction of beta-hairpin and alpha-helix content we obtained using the AGBNP model agrees well with experimental results. The thermodynamic stability of the beta-hairpin from protein G and the alpha-helical C-peptide from ribonuclease A as modeled with the OPLS-AA/AGBNP effective potential reflects the balance between the nonpolar effective potential terms, which drive compaction, and the polar and hydrogen bonding terms, which promote secondary structure formation.

Distinguish protein decoys by Using a scoring function based on a new AMBER force field, short molecular dynamics simulations, and the generalized born solvent model

Recent works have shown the ability of physics-based potentials (e.g., CHARMM and OPLS-AA) and energy minimization to differentiate the native protein structures from large ensemble of non-native structures. In this study, we extended previous work by other authors and developed an energy scoring function using a new set of AMBER parameters (also recently developed in our laboratory) in conjunction with molecular dynamics and the Generalized Born solvent model. We evaluated the performance of our new scoring function by examining its ability to distinguish between the native and decoy protein structures. Here we present a systematic comparison of our results with those obtained with use of other physics-based potentials by previous authors. A total of 7 decoy sets, 117 protein sequences, and more than 41,000 structures were evaluated. The results of our study showed that our new scoring function represents a significant improvement over previously published physics-based scoring functions.

Docking macromolecules with flexible segments

We address a major obstacle to macromolecular docking algorithms by presenting a new method that takes into account the induced conformational adjustment of flexible loops situated at a protein/macromolecule interface. The method, MC2, is based on a multiple copy representation of the loops, coupled with a Monte Carlo conformational search of the relative position of the macromolecules and their side chain conformations. The selection of optimal loop conformations takes place during Monte Carlo cycling by the iterative adjustment of the weight of each copy. We describe here the parameterization of the method and trials on a protein-DNA complex of known 3-D structure, involving the Drosophila prd paired domain protein and its target oligonucleotide Wenqing, X. et al., Cell 1995, 80, 639. We demonstrate that our algorithm can correctly configure and position this protein, despite its relatively complex interactions with both grooves of DNA.

A genetic algorithm with conformational memories for structure prediction of polypeptides

We have developed an iterative hybrid algorithm (HA) to predict the 3D structure of peptides starting from their amino acid sequence. The HA is made of a modified genetic algorithm (GA) coupled to a local optimizer. Each HA iteration is carried out in two phases. In the first phase several GA runs are performed upon the entire peptide conformational space. In the second phase we used the manifestation of what we have called conformational memories, that arises at the end of the first phase, as a way of reducing the peptide conformational space in subsequent HA iterations. Use of conformational memories speeds up and refines the localization of the structure at the putative Global Energy Minimum (GEM) since conformational barriers are avoided. The algorithm has been used to predict successfully the putative GEM for Met- and Leu-enkephalin, and to obtain useful information regarding the 3D structure for the 8mer of polyglycine and the 16 residue (AAQAA)(3)Y peptide. The number of fitness function evaluations needed to locate the putative GEMs are fewer than those reported for other heuristic methods. This study opens the possibility of using Genetic Algorithms in high level predictions of secondary structure of polypeptides.

Experimental tests of villin subdomain folding simulations

We have used laser temperature-jump to investigate the kinetics and mechanism of folding the 35 residue subdomain of the villin headpiece. The relaxation kinetics are biphasic with a sub-microsecond phase corresponding to a helix-coil transition and a slower microsecond phase corresponding to overall unfolding/refolding. At 300 K, the folding time is 4.3(+/-0.6) micros, making it the fastest folding, naturally occurring protein, with a rate close to the theoretical speed limit. This time is in remarkable agreement with the prediction of 5 (+11,-3) micros by Zagrovic et al. from atomistic molecular dynamics simulations using an implicit solvent model. We test their prediction that replacement of the C-terminal phenylalanine residue with alanine will increase the folding rate by removing a transient non-native interaction. We find that the alanine substitution has no effect on the folding rate or on the equilibrium constant. Implications of this result for the validity of the simulated folding mechanism are discussed.

Molecular dynamics simulations of peptides and proteins with a continuum electrostatic model based on screened Coulomb potentials

A continuum electrostatics approach for molecular dynamics (MD) simulations of macromolecules is presented and analyzed for its performance on a peptide and a globular protein. The approach incorporates the screened Coulomb potential (SCP) continuum model of electrostatics, which was reported earlier. The model was validated in a broad set of tests some of which were based on Monte Carlo simulations that included single amino acids, peptides, and proteins. The implementation for large-scale MD simulations presented in this article is based on a pairwise potential that makes the electrostatic model suitable for fast analytical calculation of forces. To assess the suitability of the approach, a preliminary validation is conducted, which consists of (i) a 3-ns MD simulation of the immunoglobulin-binding domain of streptococcal protein G, a 56-residue globular protein and (ii) a 3-ns simulation of Dynorphin, a biological peptide of 17 amino acids. In both cases, the results are compared with those obtained from MD simulations using explicit water (EW) molecules in an all-atom representation. The initial structure of Dynorphin was assumed to be an alpha-helix between residues 1 and 9 as suggested from NMR measurements in micelles. The results obtained in the MD simulations show that the helical structure collapses early in the simulation, a behavior observed in the EW simulation and consistent with spectroscopic data that suggest that the peptide may adopt mainly an extended conformation in water. The dynamics of protein G calculated with the SCP implicit solvent model (SCP-ISM) reveals a stable structure that conserves all the elements of secondary structure throughout the entire simulation time. The average structures calculated from the trajectories with the implicit and explicit solvent models had a cRMSD of 1.1 A, whereas each average structure had a cRMSD of about 0.8A with respect to the X-ray structure. The main conformational differences of the average structures with respect to the crystal structure occur in the loop involving residues 8-14. Despite the overall similarity of the simulated dynamics with EW and SCP models, fluctuations of side-chains are larger when the implicit solvent is used, especially in solvent exposed side-chains. The MD simulation of Dynorphin was extended to 40 ns to study its behavior in an aqueous environment. This long simulation showed that the peptide has a tendency to form an alpha-helical structure in water, but the stabilization free energy is too weak, resulting in frequent interconversions between random and helical conformations during the simulation time. The results reported here suggest that the SCP implicit solvent model is adequate to describe electrostatic effects in MD simulation of both peptides and proteins using the same set of parameters. It is suggested that the present approach could form the basis for the development of a reliable and general continuum approach for use in molecular biology, and directions are outlined for attaining this long-term goal.

Discrimination of native protein structures using atom-atom contact scoring

We introduce a method for discriminating correctly folded proteins from well designed decoy structures using atom-atom and atom-solvent contact surfaces. The measure used to quantify contact surfaces integrates the solvent accessible surface and interatomic contacts into one quantity, allowing solvent to be treated as an atom contact. A scoring function was derived from statistical contact preferences within known protein structures and validated by using established protein decoy sets, including the "Rosetta" decoys and data from the CASP4 structure predictions. The scoring function effectively distinguished native structures from all corresponding decoys in >90% of the cases, using isolated protein subunits as target structures. If contacts between subunits within quaternary structures are included, the accuracy increases to 97%. Interactions beyond atom-atom contact range were not required to distinguish native structures from the decoys using this method. The contact scoring performed as well or better than existing statistical and physicochemical potentials and may be applied as an independent means of evaluating putative structural models.