Analysis of the impact of solvent on contacts prediction in proteins

Stabilizing Role of Water Solvation on Anion-π Interactions in Proteins

In this work, anion-π interactions between sulfate groups (SO₄ ^2-) and protein aromatic amino acids (AAs) (histidine protonated (HisP), histidine neutral (HisN), tyrosine (Tyr), tryptophan (Trp), and phenylalanine (Phe)) in an aqueous environment have been analyzed using quantum chemical (QC) calculations and molecular dynamics (MD) simulations. Sulfates can occur naturally in solution and can be contained in biomolecules playing relevant roles in their biological function. In particular, the presence of sulfate groups in glycosaminoglycans such as heparin and heparan sulfate has been shown to be relevant for protein and cellular communication and, consequently, for tissue regeneration. Therefore, anion-π interactions between sulfate groups and aromatic residues represent a relevant aspect to investigate. QC results show that such an anion-π mode of interaction between SO₄ ^2- and aromatic AAs is only possible in the presence of water molecules, in the absence of any other cooperative non-covalent interactions. Protonated histidine stands out in terms of its enhancement in the magnitude of interaction strength on solvation. Other AAs such as non-protonated histidine, tyrosine, and phenylalanine can stabilize anion-π interactions on solvation, albeit with weak interaction energy. Tryptophan does not exhibit any anion-π mode of interaction with SO₄ ^2-. The order of magnitude of the interaction of aromatic AAs with SO₄ ^2- on microsolvation is HisP > HisN > Tyr > Trp > Phe. Atoms in molecules (AIM) analysis illustrates the significance of water molecules in stabilizing the divalent SO₄ ^2- anion over the π surface of the aromatic AAs. MD simulation analysis shows that the order of magnitude of the interaction of SO₄ ^2- with aromatic AAs in macroscopic solvation is HisP > HisN, Tyr, Trp > Phe, which is very much in line with the QC results. Spatial distribution function analysis illustrates that protonated histidine alone is capable of establishing the anion-π interaction with SO₄ ^2- in the solution phase. This study sheds light on the understanding of anion-π interactions between SO₄ ^2- and aromatic AAs such as His and Tyr observed in protein crystal structures and the significance of water molecules in stabilizing such interactions, which is not feasible otherwise.

Cre Recombinase and Other Tyrosine Recombinases

Tyrosine-type site-specific recombinases (T-SSRs) have opened new avenues for the predictable modification of genomes as they enable precise genome editing in heterologous hosts. These enzymes are ubiquitous in eubacteria, prevalent in archaea and temperate phages, present in certain yeast strains, but barely found in higher eukaryotes. As tools they find increasing use for the generation and systematic modification of genomes in a plethora of organisms. If applied in host organisms, they enable precise DNA cleavage and ligation without the gain or loss of nucleotides. Criteria directing the choice of the most appropriate T-SSR system for genetic engineering include that, whenever possible, the recombinase should act independent of cofactors and that the target sequences should be long enough to be unique in a given genome. This review is focused on recent advancements in our mechanistic understanding of simple T-SSRs and their application in developmental and synthetic biology, as well as in biomedical research.

Protein-ligand docking using hamiltonian replica exchange simulations with soft core potentials

Molecular dynamics (MD) simulations in explicit solvent allow studying receptor-ligand binding processes including full flexibility of the binding partners and an explicit inclusion of solvation effects. However, in MD simulations, the search for an optimal ligand-receptor complex geometry is frequently trapped in locally stable non-native binding geometries. A Hamiltonian replica-exchange (H-REMD)-based protocol has been designed to enhance the sampling of putative ligand-receptor complexes. It is based on softening nonbonded ligand-receptor interactions along the replicas and one reference replica under the control of the original force field. The efficiency of the method has been evaluated on two receptor-ligand systems and one protein-peptide complex. Starting from misplaced initial docking geometries, the H-REMD method reached in each case the known binding geometry significantly faster than a standard MD simulation. The approach could also be useful to identify and evaluate alternative binding geometries in a given binding region with small relative differences in binding free energy.

SCOWLP update: 3D classification of protein-protein, -peptide, -saccharide and -nucleic acid interactions, and structure-based binding inferences across folds

Background

Protein interactions are essential for coordinating cellular functions. Proteomic studies have already elucidated a huge amount of protein-protein interactions that require detailed functional analysis. Understanding the structural basis of each individual interaction through their structural determination is necessary, yet an unfeasible task. Therefore, computational tools able to predict protein binding regions and recognition modes are required to rationalize putative molecular functions for proteins. With this aim, we previously created SCOWLP, a structural classification of protein binding regions at protein family level, based on the information obtained from high-resolution 3D protein-protein and protein-peptide complexes.

Description

We present here a new version of SCOWLP that has been enhanced by the inclusion of protein-nucleic acid and protein-saccharide interactions. SCOWLP takes interfacial solvent into account for a detailed characterization of protein interactions. In addition, the binding regions obtained per protein family have been enriched by the inclusion of predicted binding regions, which have been inferred from structurally related proteins across all existing folds. These inferences might become very useful to suggest novel recognition regions and compare structurally similar interfaces from different families.

Conclusions

The updated SCOWLP has new functionalities that allow both, detection and comparison of protein regions recognizing different types of ligands, which include other proteins, peptides, nucleic acids and saccharides, within a solvated environment. Currently, SCOWLP allows the analysis of predicted protein binding regions based on structure-based inferences across fold space. These predictions may have a unique potential in assisting protein docking, in providing insights into protein interaction networks, and in guiding rational engineering of protein ligands. The newly designed SCOWLP web application has an improved user-friendly interface that facilitates its usage, and is available at http://www.scowlp.org.

Bound water at protein-protein interfaces: partners, roles and hydrophobic bubbles as a conserved motif

Background

There is a great interest in understanding and exploiting protein-protein associations as new routes for treating human disease. However, these associations are difficult to structurally characterize or model although the number of X-ray structures for protein-protein complexes is expanding. One feature of these complexes that has received little attention is the role of water molecules in the interfacial region.

Methodology

A data set of 4741 water molecules abstracted from 179 high-resolution (≤ 2.30 Å) X-ray crystal structures of protein-protein complexes was analyzed with a suite of modeling tools based on the HINT forcefield and hydrogen-bonding geometry. A metric termed Relevance was used to classify the general roles of the water molecules.

Results

The water molecules were found to be involved in: a) (bridging) interactions with both proteins (21%), b) favorable interactions with only one protein (53%), and c) no interactions with either protein (26%). This trend is shown to be independent of the crystallographic resolution. Interactions with residue backbones are consistent for all classes and account for 21.5% of all interactions. Interactions with polar residues are significantly more common for the first group and interactions with non-polar residues dominate the last group. Waters interacting with both proteins stabilize on average the proteins' interaction (−0.46 kcal mol⁻¹), but the overall average contribution of a single water to the protein-protein interaction energy is unfavorable (+0.03 kcal mol⁻¹). Analysis of the waters without favorable interactions with either protein suggests that this is a conserved phenomenon: 42% of these waters have SASA ≤ 10 Ų and are thus largely buried, and 69% of these are within predominantly hydrophobic environments or “hydrophobic bubbles”. Such water molecules may have an important biological purpose in mediating protein-protein interactions.

Is there an optimal substitution matrix for contact prediction with correlated mutations?

Correlated mutations in proteins are believed to occur in order to preserve the protein functional folding through evolution. Their values can be deduced from sequence and/or structural alignments and are indicative of residue contacts in the protein three-dimensional structure. A correlation among pairs of residues is routinely evaluated with the Pearson correlation coefficient and the MCLACHLAN similarity matrix. In literature, there is no justification for the adoption of the MCLACHLAN instead of other substitution matrices. In this paper, we approach the problem of computing the optimal similarity matrix for contact prediction with correlated mutations, i.e., the similarity matrix that maximizes the accuracy of contact prediction with correlated mutations. We describe an optimization procedure, based on the gradient descent method, for computing the optimal similarity matrix and perform an extensive number of experimental tests. Our tests show that there is a large number of optimal matrices that perform similarly to MCLACHLAN. We also obtain that the upper limit to the accuracy achievable in protein contact prediction is independent of the optimized similarity matrix. This suggests that the poor scoring of the correlated mutations approach may be due to the choice of the linear correlation function in evaluating correlated mutations.

Docking glycosaminoglycans to proteins: analysis of solvent inclusion

Glycosaminoglycans (GAGs) are anionic polysaccharides, which participate in key processes in the extracellular matrix by interactions with protein targets. Due to their charged nature, accurate consideration of electrostatic and water-mediated interactions is indispensable for understanding GAGs binding properties. However, solvent is often overlooked in molecular recognition studies. Here we analyze the abundance of solvent in GAG-protein interfaces and investigate the challenges of adding explicit solvent in GAG-protein docking experiments. We observe PDB GAG-protein interfaces being significantly more hydrated than protein-protein interfaces. Furthermore, by applying molecular dynamics approaches we estimate that about half of GAG-protein interactions are water-mediated. With a dataset of eleven GAG-protein complexes we analyze how solvent inclusion affects Autodock 3, eHiTs, MOE and FlexX docking. We develop an approach to de novo place explicit solvent into the binding site prior to docking, which uses the GRID program to predict positions of waters and to locate possible areas of solvent displacement upon ligand binding. To investigate how solvent placement affects docking performance, we compare these results with those obtained by taking into account information about the solvent position in the crystal structure. In general, we observe that inclusion of solvent improves the results obtained with these methods. Our data show that Autodock 3 performs best, though it experiences difficulties to quantitatively reproduce experimental data on specificity of heparin/heparan sulfate disaccharides binding to IL-8. Our work highlights the current challenges of introducing solvent in protein-GAGs recognition studies, which is crucial for exploiting the full potential of these molecules for rational engineering.