Energy Matrix of Structurally Important Side-Chain/Side-Chain Interactions in Proteins

Exploring the inhibitory potential of in silico-designed small peptides on Helicobacter pylori Hp0231 (DsbK), a periplasmic oxidoreductase involved in disulfide bond formation

Introduction: Helicobacter pylori is a bacterium that colonizes the gastric epithelium, which affects millions of people worldwide. H. pylori infection can lead to various gastrointestinal diseases, including gastric adenocarcinoma and mucosa-associated lymphoid tissue lymphoma. Conventional antibiotic therapies face challenges due to increasing antibiotic resistance and patient non-compliance, necessitating the exploration of alternative treatment approaches. In this study, we focused on Hp0231 (DsbK), an essential component of the H. pylori Dsb (disulfide bond) oxidative pathway, and investigated peptide-based inhibition as a potential therapeutic strategy. Methods: Three inhibitory peptides designed by computational modeling were evaluated for their effectiveness using a time-resolved fluorescence assay. We also examined the binding affinity between Hp0231 and the peptides using microscale thermophoresis. Results and discussion: Our findings demonstrate that in silico-designed synthetic peptides can effectively inhibit Hp0231-mediated peptide oxidation. Targeting Hp0231 oxidase activity could attenuate H. pylori virulence without compromising bacterial viability. Therefore, peptide-based inhibitors of Hp0231 could be candidates for the development of new targeted strategy, which does not influence the composition of the natural human microbiome, but deprive the bacterium of its pathogenic properties.

Atligator Web: A Graphical User Interface for Analysis and Design of Protein-Peptide Interactions

A key functionality of proteins is based on their ability to form interactions with other proteins or peptides. These interactions are neither easily described nor fully understood, which is why the design of specific protein-protein interaction interfaces remains a challenge for protein engineering. We recently developed the software ATLIGATOR to extract common interaction patterns between different types of amino acids and store them in a database. The tool enables the user to better understand frequent interaction patterns and find groups of interactions. Furthermore, frequent motifs can be directly transferred from the database to a user-defined scaffold as a starting point for the engineering of new binding capabilities. Since three-dimensional visualization is a crucial part of ATLIGATOR, we created ATLIGATOR web-a web server offering an intuitive graphical user interface (GUI) available at https://atligator.uni-bayreuth.de. This new interface empowers users to apply ATLIGATOR by providing easy access with having all parts directly connected. Moreover, we extended the web by a design functionality so that, overall, ATLIGATOR web facilitates the use of ATLIGATOR with a more intuitive UI and advanced design options.

ATLIGATOR: editing protein interactions with an atlas-based approach

MOTIVATION

Recognition of specific molecules by proteins is a fundamental cellular mechanism and relevant for many applications. Being able to modify binding is a key interest and can be achieved by repurposing established interaction motifs. We were specifically interested in a methodology for the design of peptide binding modules. By leveraging interaction data from known protein structures, we plan to accelerate the design of novel protein or peptide binders.

RESULTS

We developed ATLIGATOR-a computational method to support the analysis and design of a protein's interaction with a single side chain. Our program enables the building of interaction atlases based on structures from the PDB. From these atlases pocket definitions are extracted that can be searched for frequent interactions. These searches can reveal similarities in unrelated proteins as we show here for one example. Such frequent interactions can then be grafted onto a new protein scaffold as a starting point of the design process. The ATLIGATOR tool is made accessible through a python API as well as a CLI with python scripts.

AVAILABILITY AND IMPLEMENTATION

Source code can be downloaded at github (https://www.github.com/Hoecker-Lab/atligator), installed from PyPI ('atligator') and is implemented in Python 3.

Theoretical Investigation of the Coronavirus SARS-CoV-2 (COVID-19) Infection Mechanism and Selectivity

The SARS-CoV-2 virus, commonly known as COVID-19, first occurred in December 2019 in Wuhan, Hubei Province, China. Since then, it has become a tremendous threat to human health. With a pandemic threat, it is in the significant interest of the scientific world to establish its method of infection. In this manuscript, we combine knowledge of the infection mechanism with theoretical methods to answer the question of the virus’s selectivity. We proposed a two-stage infection mechanism. In the first step, the virus interacts with the ACE2 receptor, with the “proper strength”. When the interaction is too strong, the virus will remain in an “improper position”; if the interaction is too weak, the virus will “run away” from the cell. We also indicated three residues (positions 30, 31, and 353) located on the ACE2 protein-binding interface, which seems to be crucial for successful infection. Our results indicate that these residues are necessary for the initiation of the infection process.

Fast and Accurate Quantum Mechanical Modeling of Large Molecular Systems Using Small Basis Set Hartree-Fock Methods Corrected with Atom-Centered Potentials

There has been significant interest in developing fast and accurate quantum mechanical methods for modeling large molecular systems. In this work, by utilizing a machine learning regression technique, we have developed new low-cost quantum mechanical approaches to model large molecular systems. The developed approaches rely on using one-electron Gaussian-type functions called atom-centered potentials (ACPs) to correct for the basis set incompleteness and the lack of correlation effects in the underlying minimal or small basis set Hartree-Fock (HF) methods. In particular, ACPs are proposed for ten elements common in organic and bioorganic chemistry (H, B, C, N, O, F, Si, P, S, and Cl) and four different base methods: two minimal basis sets (MINIs and MINIX) plus a double-ζ basis set (6-31G*) in combination with dispersion-corrected HF (HF-D3/MINIs, HF-D3/MINIX, HF-D3/6-31G*) and the HF-3c method. The new ACPs are trained on a very large set (73 832 data points) of noncovalent properties (interaction and conformational energies) and validated additionally on a set of 32 048 data points. All reference data are of complete basis set coupled-cluster quality, mostly CCSD(T)/CBS. The proposed ACP-corrected methods are shown to give errors in the tenths of a kcal/mol range for noncovalent interaction energies and up to 2 kcal/mol for molecular conformational energies. More importantly, the average errors are similar in the training and validation sets, confirming the robustness and applicability of these methods outside the boundaries of the training set. In addition, the performance of the new ACP-corrected methods is similar to complete basis set density functional theory (DFT) but at a cost that is orders of magnitude lower, and the proposed ACPs can be used in any computational chemistry program that supports effective-core potentials without modification. It is also shown that ACPs improve the description of covalent and noncovalent bond geometries of the underlying methods and that the improvement brought about by the application of the ACPs is directly related to the number of atoms to which they are applied, allowing the treatment of systems containing some atoms for which ACPs are not available. Overall, the ACP-corrected methods proposed in this work constitute an alternative accurate, economical, and reliable quantum mechanical approach to describe the geometries, interaction energies, and conformational energies of systems with hundreds to thousands of atoms.

Amino Acid Interaction (INTAA) web server

Large biomolecules-proteins and nucleic acids-are composed of building blocks which define their identity, properties and binding capabilities. In order to shed light on the energetic side of interactions of amino acids between themselves and with deoxyribonucleotides, we present the Amino Acid Interaction web server (http://bioinfo.uochb.cas.cz/INTAA/). INTAA offers the calculation of the residue Interaction Energy Matrix for any protein structure (deposited in Protein Data Bank or submitted by the user) and a comprehensive analysis of the interfaces in protein-DNA complexes. The Interaction Energy Matrix web application aims to identify key residues within protein structures which contribute significantly to the stability of the protein. The application provides an interactive user interface enhanced by 3D structure viewer for efficient visualization of pairwise and net interaction energies of individual amino acids, side chains and backbones. The protein-DNA interaction analysis part of the web server allows the user to view the relative abundance of various configurations of amino acid-deoxyribonucleotide pairs found at the protein-DNA interface and the interaction energies corresponding to these configurations calculated using a molecular mechanical force field. The effects of the sugar-phosphate moiety and of the dielectric properties of the solvent on the interaction energies can be studied for the various configurations.

Impact of selected amino acids of HP0377 (Helicobacter pylori thiol oxidoreductase) on its functioning as a CcmG (cytochrome c maturation) protein and Dsb (disulfide bond) isomerase

Helicobacter pylori HP0377 is a thiol oxidoreductase, a member of the CcmG family involved in cytochrome biogenesis, as previously shown by in vitro experiments. In this report, we document that HP0377 also acts in vivo in the cytochrome assembly process in Bacillus subtilis, where it complements the lack of ResA. However, unlike other characterized proteins in this family, HP0377 is a dithiol reductase and isomerase. We elucidated how the amino acid composition of its active site modulates its functionality. We demonstrated that cis-proline (P156) is involved in its interaction with the redox partner (CcdA), as a P156T HP0377 variant is inactive in vivo and is present in the oxidized form in B. subtilis. Furthermore, we showed that engineering the HP0377 active motif by changing CSYC motif into CSYS or SSYC, clearly diminishes two activities (reduction and isomerization) of the protein. Whereas HP0377CSYA is inactive in reduction as well as in isomerization, HP0377CSYS retains reductive activity. Also, replacement of F95 by Q decreases its ability to regenerate scRNase and does not influence the reductive activity of HP0377CSYS towards apocytochrome c. HP0377 is also distinguished from other CcmGs as it forms a 2:1 complex with apocytochrome c. Phylogenetic analyses showed that, although HP0377 is capable of complementing ResA in Bacillus subtilis, its thioredoxin domain has a different origin, presumably common to DsbC.

Biochemical properties of the HtrA homolog from bacterium Stenotrophomonas maltophilia

The HtrA proteins due to their proteolytic, and in many cases chaperone activity, efficiently counteract consequences of stressful conditions. In the environmental bacterium and nosocomial pathogen Stenotrophomonas maltophilia HtrA (HtrA_Sm) is induced as a part of adaptive response to host temperature (37°C). We examined the biochemical properties of HtrA_Sm and compared them with those of model HtrA_Ec from Escherichia coli. We found that HtrA_Sm is a protease and chaperone that operates over a wide range of pH and is highly active at temperatures between 35 and 37°C. The temperature-sensitive activity corresponded well with the lower thermal stability of the protein and weaker stability of the oligomer. Interestingly, the enzyme shows slightly different substrate cleavage specificity when compared to other bacterial HtrAs. A computational model of the three-dimensional structure of HtrA_Sm indicates differences in the S1 substrate specificity pocket and suggests weaker inter-trimer interactions when compared to HtrA_Ec. The observed features of HtrA_Sm suggest that this protein may play a protective role under stressful conditions acting both as a protease and a chaperone. The optimal temperatures for the protein activity may reflect the evolutionary adaptation of S. maltophilia to life in soil or aqueous environments, where the temperatures are usually much below 37°C.

Large-Scale Quantitative Assessment of Binding Preferences in Protein-Nucleic Acid Complexes

The growing number of high-quality experimental (X-ray, NMR) structures of protein–DNA complexes has sufficient enough information to assess whether universal rules governing the DNA sequence recognition process apply. While previous studies have investigated the relative abundance of various modes of amino acid–base contacts (van der Waals contacts, hydrogen bonds), relatively little is known about the energetics of these noncovalent interactions. In the present study, we have performed the first large-scale quantitative assessment of binding preferences in protein–DNA complexes by calculating the interaction energies in all 80 possible amino acid–DNA base combinations. We found that several mutual amino acid–base orientations featuring bidentate hydrogen bonds capable of unambiguous one-to-one recognition correspond to unique minima in the potential energy space of the amino acid–base pairs. A clustering algorithm revealed that these contacts form a spatially well-defined group offering relatively little conformational freedom. Various molecular mechanics force field and DFT-D ab initio calculations were performed, yielding similar results.

N-terminal Prion Protein Peptides (PrP(120-144)) Form Parallel In-register β-Sheets via Multiple Nucleation-dependent Pathways

The prion diseases are a family of fatal neurodegenerative diseases associated with the misfolding and accumulation of normal prion protein (PrP^C) into its pathogenic scrapie form (PrP^Sc). Understanding the fundamentals of prion protein aggregation and the molecular architecture of PrP^Sc is key to unraveling the pathology of prion diseases. Our work investigates the early-stage aggregation of three prion protein peptides, corresponding to residues 120-144 of human (Hu), bank vole (BV), and Syrian hamster (SHa) prion protein, from disordered monomers to β-sheet-rich fibrillar structures. Using 12 μs discontinuous molecular dynamics simulations combined with the PRIME20 force field, we find that the Hu-, BV-, and SHaPrP(120-144) aggregate via multiple nucleation-dependent pathways to form U-shaped, S-shaped, and Ω-shaped protofilaments. The S-shaped HuPrP(120-144) protofilament is similar to the amyloid core structure of HuPrP(112-141) predicted by Zweckstetter. HuPrP(120-144) has a shorter aggregation lag phase than BVPrP(120-144) followed by SHaPrP(120-144), consistent with experimental findings. Two amino acid substitutions I138M and I139M retard the formation of parallel in-register β-sheet dimers during the nucleation stage by increasing side chain-side chain association and reducing side chain interaction specificity. On average, HuPrP(120-144) aggregates contain more parallel β-sheet content than those formed by BV- and SHaPrP(120-144). Deletion of the C-terminal residues 138-144 prevents formation of fibrillar structures in agreement with the experiment. This work sheds light on the amyloid core structures underlying prion strains and how I138M, I139M, and S143N affect prion protein aggregation kinetics.

The LD loop as an important structural element required for transmission of the allosteric signal in the HtrA (DegP) protease from Escherichia coli

High-temperature requirement A (HtrA; DegP) from Escherichia coli, an important element of the extracytoplasmic protein quality-control system, is a member of the evolutionarily conserved family of serine proteases. The characteristic feature of this protein is its allosteric mode of activation. The regulatory loops, L3, L2, L1 and LD, play a crucial role in the transmission of the allosteric signal. Yet, the role of LD has not been fully elucidated. Therefore, we undertook a study to explain the role of the individual LD residues in inducing and maintaining the proteolytic activity of HtrA. We investigated the influence of amino acid substitutions located within the LD loop on the kinetics of a model substrate cleavage as well as on the dynamics of the oligomeric structure of HtrA. We found that the mutations that were expected to disturb the loop's structure and/or interactions with the remaining regulatory loops severely diminished the proteolytic activity of HtrA. The opposite effect, that is, increased activity, was observed for G174S substitution, which was predicted to strengthen the interactions mediated by LD. HtrAG174S protein had an equilibrium shifted toward the active enzyme and formed preferentially high-order oligomeric forms.

Sequence-Specific Recognition of DNA by Proteins: Binding Motifs Discovered Using a Novel Statistical/Computational Analysis

Decades of intensive experimental studies of the recognition of DNA sequences by proteins have provided us with a view of a diverse and complicated world in which few to no features are shared between individual DNA-binding protein families. The originally conceived direct readout of DNA residue sequences by amino acid side chains offers very limited capacity for sequence recognition, while the effects of the dynamic properties of the interacting partners remain difficult to quantify and almost impossible to generalise. In this work we investigated the energetic characteristics of all DNA residue—amino acid side chain combinations in the conformations found at the interaction interface in a very large set of protein—DNA complexes by the means of empirical potential-based calculations. General specificity-defining criteria were derived and utilised to look beyond the binding motifs considered in previous studies. Linking energetic favourability to the observed geometrical preferences, our approach reveals several additional amino acid motifs which can distinguish between individual DNA bases. Our results remained valid in environments with various dielectric properties.

N-Terminally extended analogues of the K⁺ channel toxin from Stichodactyla helianthus as potent and selective blockers of the voltage-gated potassium channel Kv1.3

The voltage-gated potassium channel Kv1.3 is an important target for the treatment of autoimmune diseases and asthma. Blockade of Kv1.3 by the sea anemone peptide K⁺-channel toxin from Stichodactyla helianthus (ShK) inhibits the proliferation of effector memory T lymphocytes and ameliorates autoimmune diseases in animal models. However, the lack of selectivity of ShK for Kv1.3 over the Kv1.1 subtype has driven a search for Kv1.3-selective analogues. In the present study, we describe N-terminally extended analogues of ShK that contain a negatively-charged Glu, designed to mimic the phosphonate adduct in earlier Kv1.3-selective analogues, and consist entirely of common protein amino acids. Molecular dynamics simulations indicated that a Trp residue at position [-3] of the tetrapeptide extension could form stable interactions with Pro377 of Kv1.3 and best discriminates between Kv1.3 and Kv1.1. This led to the development of ShK with an N-terminal Glu-Trp-Ser-Ser extension ([EWSS]ShK), which inhibits Kv1.3 with an IC₅₀ of 34 pm and is 158-fold selective for Kv1.3 over Kv1.1. In addition, [EWSS]ShK is more than 2900-fold more selective for Kv1.3 over Kv1.2 and KCa3.1 channels. As a highly Kv1.3-selective analogue of ShK based entirely on protein amino acids, which can be produced by recombinant expression, this peptide is a valuable addition to the complement of therapeutic candidates for the treatment of autoimmune diseases.

Influence of side-chain interactions on the self-assembly of discotic tricarboxyamides: a crystallographic insight

Side chains interactions promote the self-assembly of discotic tricarboxyamides to form an entangled fiber network and thermo responsive gel.

What are the preferred horizontal displacements of aromatic–aromatic interactions in proteins? Comparison with the calculated benzene–benzene potential energy surface

Stacking interactions of phenylalanine residues show preference for large offsets (3.5–5.0 Å), while the calculations show substantially strong interactions, of about −2.0 kcal mol⁻¹.

Gas-phase salt bridge interactions between glutamic acid and arginine

The gas-phase side chain-side chain (SC-SC) interaction and possible proton transfer between glutamic acid (Glu) and arginine (Arg) residues are studied under low-temperature conditions in an overall neutral peptide. Conformation-specific IR spectra, obtained with the free electron laser FELIX, in combination with density functional theory (DFT) calculations, provide insight into the occurrence of intramolecular proton transfer and detailed information on the conformational preferences of the peptides Z-Glu-Alan-Arg-NHMe (n = 0,1,3). Low-energy structures are obtained using molecular dynamics simulations via the simulated annealing approach, resulting in three types of SC-SC interactions, in particular two types of pair-wise interactions and one bifurcated interaction. These low-energy structures are optimized and frequency calculations are performed using the B3LYP functional, for structural analysis, and the M05-2x functional, for relative energies, employing the 6-311+G(d,p) basis set. Comparison of experimental and computed spectra suggests that only a single conformation was present for each of the three peptides. Despite the increasing spacing between the Glu and Arg residues, the peptides have several types of interactions in common, in particular specific SC-SC and dispersion interactions between the Arg side chain and the phenyl ring of the Z-cap. Comparison with previous experiments on Ac-Glu-Ala-Phe-Ala-Arg-NHMe as well as molecular dynamics simulations further suggest that the pairwise interaction observed here is indeed energetically most favorable for short peptide sequences.

The accuracy of quantum chemical methods for large noncovalent complexes

We evaluate the performance of the most widely used wavefunction, density functional theory, and semiempirical methods for the description of noncovalent interactions in a set of larger, mostly dispersion-stabilized noncovalent complexes (the L7 data set). The methods tested include MP2, MP3, SCS-MP2, SCS(MI)-MP2, MP2.5, MP2.X, MP2C, DFT-D, DFT-D3 (B3-LYP-D3, B-LYP-D3, TPSS-D3, PW6B95-D3, M06-2X-D3) and M06-2X, and semiempirical methods augmented with dispersion and hydrogen bonding corrections: SCC-DFTB-D, PM6-D, PM6-DH2 and PM6-D3H4. The test complexes are the octadecane dimer, the guanine trimer, the circumcoronene…adenine dimer, the coronene dimer, the guanine-cytosine dimer, the circumcoronene…guanine-cytosine dimer, and an amyloid fragment trimer containing phenylalanine residues. The best performing method is MP2.5 with relative root mean square deviation (rRMSD) of 4 %. It can thus be recommended as an alternative to the CCSD(T)/CBS (alternatively QCISD(T)/CBS) benchmark for molecular systems which exceed current computational capacity. The second best non-DFT method is MP2C with rRMSD of 8 %. A method with the most favorable "accuracy/cost" ratio belongs to the DFT family: BLYP-D3, with an rRMSD of 8 %. Semiempirical methods deliver less accurate results (the rRMSD exceeds 25 %). Nevertheless, their absolute errors are close to some much more expensive methods such as M06-2X, MP2 or SCS(MI)-MP2, and thus their price/performance ratio is excellent.

On the importance and origin of aromatic interactions in chemistry and biodisciplines

Aromatic systems contain both σ- and π-electrons, which in turn constitute σ- and π-molecular orbitals (MOs). In discussing the properties of these systems, researchers typically refer to the highest occupied and lowest unoccupied MOs, which are π MOs. The characteristic properties of aromatic systems, such as their low ionization potentials and electron affinities, high polarizabilities and stabilities, and small band gaps (in spectroscopy called the N → V1 space), can easily be explained based on their electronic structure. These one-electron properties point to characteristic features of how aromatic systems interact with each other. Unlike hydrogen bonding systems, which primarily interact through electrostatic forces, complexes containing aromatic systems, especially aromatic stacked pairs, are predominantly stabilized by dispersion attraction. The stabilization energy in the benzene dimer is rather small (~2.5 kcal/mol) but strengthens with heteroatom substitution. The stacked interaction of aromatic nucleic acid bases is greater than 10 kcal/mol, and for the most stable stacked pair, guanine and cytosine, it reaches approximately 17 kcal/mol. Although these values do not equal the planar H-bonded interactions of these bases (~29 kcal/mol), stacking in DNA is more frequent than H-bonding and, unlike H-bonding, is not significantly weakened when passing from the gas phase to a water environment. Consequently, the stacking of aromatic systems represents the leading stabilization energy contribution in biomacromolecules and in related nanosystems. Therefore stacking (dispersion) interactions predominantly determine the double helical structure of DNA, which underlies its storage and transfer of genetic information. Similarly, dispersion is the dominant contributor to attractive interactions involving aromatic amino acids within the hydrophobic core of a protein, which is critical for folding. Therefore, understanding the nature of aromatic interactions, which depend greatly on quantum mechanical (QM) calculations, is of key importance in biomolecular science. This Account shows that accurate binding energies for aromatic complexes should be based on computations made at the (estimated) CCSD(T)/complete basis set limit (CBS) level of theory. This method is the least computationally intensive one that can give accurate stabilization energies for all common classes of noncovalent interactions (aromatic-aromatic, H-bonding, ionic, halogen bonding, charge-transfer, etc.). These results allow for direct comparison of binding energies between different interaction types. Conclusions based on lower-level QM calculations should be considered with care.

Towards a better understanding of the specificity of protein-protein interaction

In order to predict interaction interface for proteins, it is crucial to identify their characteristic features controlling the interaction process. We present analysis of 69 crystal structures of dimer protein complexes that provides a basis for reasonable description of the phenomenon. Interaction interfaces of two proteins at amino acids level were localized and described in terms of their chemical composition, binding preferences, and residue interaction energies utilizing Amber empirical force field. The characteristic properties of the interaction interface were compared against set of corresponding intramolecular binding parameters for amino acids in proteins. It has been found that geometrically distinct clusters of large hydrophobic amino acids (leucine, valine, isoleucine, and phenylalanine) as well as polar tyrosines and charged arginines are signatures of the protein-protein interaction interface. At some extent, we can generalize that protein-protein interaction (seen through interaction between amino acids) is very similar to the intramolecular arrangement of amino acids, although intermolecular pairs have generally lower interaction energies with their neighbors. Interfaces, therefore, possess high degree of complementarity suggesting also high selectivity of the process. The utilization of our results can improve interface prediction algorithms and improve our understanding of protein-protein recognition.

Genomes to hits in silico - a country path today, a highway tomorrow: a case study of chikungunya

These are exciting times for bioinformaticians, computational biologists and drug designers with the genome and proteome sequences and related structural databases growing at an accelerated pace. The post-genomic era has triggered high expectations for a rapid and successful treatment of diseases. However, in this biological information rich and functional knowledge poor scenario, the challenges are indeed grand, no less than the assembly of the genome of the whole organism. These include functional annotation of genes, identification of druggable targets, prediction of three-dimensional structures of protein targets from their amino acid sequences, arriving at lead compounds for these targets followed by a transition from bench to bedside. We propose here a "Genome to Hits In Silico" strategy (called Dhanvantari) and illustrate it on Chikungunya virus (CHIKV). "Genome to hits" is a novel pathway incorporating a series of steps such as gene prediction, protein tertiary structure determination, active site identification, hit molecule generation, docking and scoring of hits to arrive at lead compounds. The current state of the art for each of the steps in the pathway is high-lighted and the feasibility of creating an automated genome to hits assembly line is discussed.

Optimal definition of inter-residual contact in globular proteins based on pairwise interaction energy calculations, its robustness, and applications

Although a contact is an essential measurement for the topology as well as strength of non-covalent interactions in biomolecules and their complexes, there is no general agreement in the definition of this feature. Most of the definitions work with simple geometric criteria which do not fully reflect the energy content or ability of the biomolecular building blocks to arrange their environment. We offer a reasonable solution to this problem by distinguishing between "productive" and "non-productive" contacts based on their interaction energy strength and properties. We have proposed a method which converts the protein topology into a contact map that represents interactions with statistically significant high interaction energies. We do not prove that these contacts are exclusively stabilizing, but they represent a gateway to thermodynamically important rather than geometry-based contacts. The process is based on protein fragmentation and calculation of interaction energies using the OPLS force field and relies on pairwise additivity of amino acid interactions. Our approach integrates the treatment of different types of interactions, avoiding the problems resulting from different contributions to the overall stability and the different effect of the environment. The first applications on a set of homologous proteins have shown the usefulness of this classification for a sound estimate of protein stability.

The energy computation paradox and ab initio protein folding

The routine prediction of three-dimensional protein structure from sequence remains a challenge in computational biochemistry. It has been intuited that calculated energies from physics-based scoring functions are able to distinguish native from nonnative folds based on previous performance with small proteins and that conformational sampling is the fundamental bottleneck to successful folding. We demonstrate that as protein size increases, errors in the computed energies become a significant problem. We show, by using error probability density functions, that physics-based scores contain significant systematic and random errors relative to accurate reference energies. These errors propagate throughout an entire protein and distort its energy landscape to such an extent that modern scoring functions should have little chance of success in finding the free energy minima of large proteins. Nonetheless, by understanding errors in physics-based score functions, they can be reduced in a post-hoc manner, improving accuracy in energy computation and fold discrimination.