Protein-spanning water networks and implications for prediction of protein-protein interactions mediated through hydrophobic effects

Extending the PARCH Scale: Assessing Hydropathy of Proteins across Multiple Water Models

Quantitative assessment of amino acid hydropathy can be done using the protocol for assigning a residue's character on a hydropathy (PARCH) scale, which assigns values from 0 to 10, with lower values indicating greater hydrophobicity. The merit of the PARCH scale lies in its ability to integrate both the nanoscale topographical features and the chemical properties of amino acid residues when determining hydropathy. In its initial application, we employed the TIP3P water model, optimized for CHARMM36m proteins, to simulate the water behavior around the protein surface. Due to the growing use of the PARCH scale, we have extended its application to three additional all-atom water models: TIP4P, TIP4P-Ew, and TIP5P. Our findings reveal that although PARCH values vary across these water models, the relative hydropathy trends remain consistent. All models successfully distinguished hydrophobic from hydrophilic regions in nanoscale topography, although charged residues showed greater sensitivity to model choice, leading to more significant value variances. Additionally, we evaluated the influence of two other parameters─the force constant used to constrain proteins and the time step of the evaporation process─on the PARCH scale. Overall, the PARCH scale has demonstrated robustness in capturing protein hydropathy across various water models, suggesting its potential applicability with other protein-water force field combinations and even molecular systems beyond proteins.

An Affordable Topography-Based Protocol for Assigning a Residue's Character on a Hydropathy (PARCH) Scale

The hydropathy of proteins or quantitative assessment of protein-water interactions has been a topic of interest for decades. Most hydropathy scales use a residue-based or atom-based approach to assign fixed numerical values to the 20 amino acids and categorize them as hydrophilic, hydroneutral, or hydrophobic. These scales overlook the protein's nanoscale topography, such as bumps, crevices, cavities, clefts, pockets, and channels, in calculating the hydropathy of the residues. Some recent studies have included protein topography in determining hydrophobic patches on protein surfaces, but these methods do not provide a hydropathy scale. To overcome the limitations in the existing methods, we have developed a Protocol for Assigning a Residue's Character on the Hydropathy (PARCH) scale that adopts a holistic approach to assigning the hydropathy of a residue. The parch scale evaluates the collective response of the water molecules in the protein's first hydration shell to increasing temperatures. We performed the parch analysis of a set of well-studied proteins that include the following─enzymes, immune proteins, and integral membrane proteins, as well as fungal and virus capsid proteins. Since the parch scale evaluates every residue based on its location, a residue may have very different parch values inside a crevice versus a surface bump. Thus, a residue can have a range of parch values (or hydropathies) dictated by the local geometry. The parch scale calculations are computationally inexpensive and can compare hydropathies of different proteins. The parch analysis can affordably and reliably aid in designing nanostructured surfaces, identifying hydrophilic and hydrophobic patches, and drug discovery.

Water, Protons, and the Gating of Voltage-Gated Potassium Channels

Ion channels are ubiquitous throughout all forms of life. Potassium channels are even found in viruses. Every cell must communicate with its surroundings, so all cells have them, and excitable cells, in particular, especially nerve cells, depend on the behavior of these channels. Every channel must be open at the appropriate time, and only then, so that each channel opens in response to the stimulus that tells that channel to open. One set of channels, including those in nerve cells, responds to voltage. There is a standard model for the gating of these channels that has a section of the protein moving in response to the voltage. However, there is evidence that protons are moving, rather than protein. Water is critical as part of the gating process, although it is hard to see how this works in the standard model. Here, we review the extensive evidence of the importance of the role of water and protons in gating these channels. Our principal example, but by no means the only example, will be the Kv1.2 channel. Evidence comes from the effects of D2O, from mutations in the voltage sensing domain, as well as in the linker between that domain and the gate, and at the gate itself. There is additional evidence from computations, especially quantum calculations. Structural evidence comes from X-ray studies. The hydration of ions is critical in the transfer of ions in constricted spaces, such as the gate region and the pore of a channel; we will see how the structure of the hydrated ion fits with the structure of the channel. In addition, there is macroscopic evidence from osmotic experiments and streaming current measurements. The combined evidence is discussed in the context of a model that emphasizes the role of protons and water in gating these channels.

Spatial Layouts of Low-Entropy Hydration Shells Guide Protein Binding

Protein-protein binding enables orderly biological self-organization and is therefore considered a miracle of nature. Protein‒protein binding is driven by electrostatic forces, hydrogen bonding, van der Waals force, and hydrophobic interactions. Among these physical forces, only hydrophobic interactions can be considered long-range intermolecular attractions between proteins due to the electrostatic shielding of surrounding water molecules. Low-entropy hydration shells around proteins drive hydrophobic attraction among them that essentially coordinate protein‒protein binding. Here, an innovative method is developed for identifying low-entropy regions of hydration shells of proteins by screening off pseudohydrophilic groups on protein surfaces and revealing that large low-entropy regions of the hydration shells typically cover the binding sites of individual proteins. According to an analysis of determined protein complex structures, shape matching between a large low-entropy hydration shell region of a protein and that of its partner at the binding sites is revealed as a universal law. Protein‒protein binding is thus found to be mainly guided by hydrophobic collapse between the shape-matched low-entropy hydration shells that is verified by bioinformatics analyses of hundreds of structures of protein complexes, which cover four test systems. A simple algorithm is proposed to accurately predict protein binding sites.

Modeling of the thermal properties of SARS-CoV-2 S-protein

We calculate the thermal and conformational states of the spike glycoprotein (S-protein) of SARS-CoV-2 at seven temperatures ranging from 3°C to 95°C by all-atom molecular dynamics (MD) µs-scale simulations with the objectives to understand the structural variations on the temperatures and to determine the potential phase transition while trying to correlate such findings of the S-protein with the observed properties of the SARS-CoV2. Our simulations revealed the following thermal properties of the S-protein: 1) It is structurally stable at 3°C, agreeing with observations that the virus stays active for more than two weeks in the cold supply chain; 2) Its structure varies more significantly at temperature values of 60°C-80°C; 3) The sharpest structural variations occur near 60°C, signaling a plausible critical temperature nearby; 4) The maximum deviation of the receptor-binding domain at 37°C, corroborating the anecdotal observations that the virus is most infective at 37°C; 5) The in silico data agree with reported experiments of the SARS-CoV-2 survival times from weeks to seconds by our clustering approach analysis. Our MD simulations at µs scales demonstrated the S-protein's thermodynamics of the critical states at around 60°C, and the stable and denatured states for temperatures below and above this value, respectively.

SARS-CoV-2 Variants, RBD Mutations, Binding Affinity, and Antibody Escape

Since 2020, the receptor-binding domain (RBD) of the spike protein of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been constantly mutating, producing most of the notable missense mutations in the context of "variants of concern", probably in response to the vaccine-driven alteration of immune profiles of the human population. The Delta variant, in particular, has become the most prevalent variant of the epidemic, and it is spreading in countries with the highest vaccination rates, causing the world to face the risk of a new wave of the contagion. Understanding the physical mechanism responsible for the mutation-induced changes in the RBD's binding affinity, its transmissibility, and its capacity to escape vaccine-induced immunity is the "urgent challenge" in the development of preventive measures, vaccines, and therapeutic antibodies against the coronavirus disease 2019 (COVID-19) pandemic. In this study, entropy-enthalpy compensation and the Gibbs free energy change were used to analyze the impact of the RBD mutations on the binding affinity of SARS-CoV-2 variants with the receptor angiotensin converting enzyme 2 (ACE2) and existing antibodies. Through the analysis, we found that the existing mutations have already covered almost all possible detrimental mutations that could result in an increase of transmissibility, and that a possible mutation in amino-acid position 498 of the RBD can potentially enhance its binding affinity. A new calculation method for the binding energies of protein-protein complexes is proposed based on the entropy-enthalpy compensation rule. All known structures of RBD-antibody complexes and the RBD-ACE2 complex comply with the entropy-enthalpy compensation rule in providing the driving force behind the spontaneous protein-protein docking. The variant-induced risk of breakthrough infections in vaccinated people is attributed to the L452R mutation's reduction of the binding affinity of many antibodies. Mutations reversing the hydrophobic or hydrophilic performance of residues in the spike RBD potentially cause breakthrough infections of coronaviruses due to the changes in geometric complementarity in the entropy-enthalpy compensations between antibodies and the virus at the binding sites.

Entropy-Enthalpy Compensations Fold Proteins in Precise Ways

Exploring the protein-folding problem has been a longstanding challenge in molecular biology and biophysics. Intramolecular hydrogen (H)-bonds play an extremely important role in stabilizing protein structures. To form these intramolecular H-bonds, nascent unfolded polypeptide chains need to escape from hydrogen bonding with surrounding polar water molecules under the solution conditions that require entropy-enthalpy compensations, according to the Gibbs free energy equation and the change in enthalpy. Here, by analyzing the spatial layout of the side-chains of amino acid residues in experimentally determined protein structures, we reveal a protein-folding mechanism based on the entropy-enthalpy compensations that initially driven by laterally hydrophobic collapse among the side-chains of adjacent residues in the sequences of unfolded protein chains. This hydrophobic collapse promotes the formation of the H-bonds within the polypeptide backbone structures through the entropy-enthalpy compensation mechanism, enabling secondary structures and tertiary structures to fold reproducibly following explicit physical folding codes and forces. The temperature dependence of protein folding is thus attributed to the environment dependence of the conformational Gibbs free energy equation. The folding codes and forces in the amino acid sequence that dictate the formation of β-strands and α-helices can be deciphered with great accuracy through evaluation of the hydrophobic interactions among neighboring side-chains of an unfolded polypeptide from a β-strand-like thermodynamic metastable state. The folding of protein quaternary structures is found to be guided by the entropy-enthalpy compensations in between the docking sites of protein subunits according to the Gibbs free energy equation that is verified by bioinformatics analyses of a dozen structures of dimers. Protein folding is therefore guided by multistage entropy-enthalpy compensations of the system of polypeptide chains and water molecules under the solution conditions.

Identifying hydrophobic protein patches to inform protein interaction interfaces

Interactions between proteins lie at the heart of numerous biological processes and are essential for the proper functioning of the cell. Although the importance of hydrophobic residues in driving protein interactions is universally accepted, a characterization of protein hydrophobicity, which informs its interactions, has remained elusive. The challenge lies in capturing the collective response of the protein hydration waters to the nanoscale chemical and topographical protein patterns, which determine protein hydrophobicity. To address this challenge, here, we employ specialized molecular simulations wherein water molecules are systematically displaced from the protein hydration shell; by identifying protein regions that relinquish their waters more readily than others, we are then able to uncover the most hydrophobic protein patches. Surprisingly, such patches contain a large fraction of polar/charged atoms and have chemical compositions that are similar to the more hydrophilic protein patches. Importantly, we also find a striking correspondence between the most hydrophobic protein patches and regions that mediate protein interactions. Our work thus establishes a computational framework for characterizing the emergent hydrophobicity of amphiphilic solutes, such as proteins, which display nanoscale heterogeneity, and for uncovering their interaction interfaces.

Identifying the Molecular Orientation and Clusters in the Liquid-Vapor Interface of 1-Propanol by Time-Delayed Mass Spectrometry

Structural inhomogeneity of the liquid-vapor interface, such as the spatial orientation of molecular specific groups and the non-uniform distribution of hydrogen-bonded (HB) clusters, is crucial for understanding the physicochemical processes therein. Although the molecular orientation at the outermost layer was authenticated, to date, direct experimental evidence of the in situ existence of different-sized HB clusters, as a long-standing theoretical argument, is still lacking. Here we report time-delayed electron-impact tandem mass spectrometry, and its powerful ability to identify the local structures of the liquid-vapor interface of 1-propanol is demonstrated not only by mapping the molecular orientations both in the outermost layer and in the subsurface but also by validating the existence of the HB molecular dimers in the subsurface by detecting their protonated ions. We further distinguish two different sources of the protonated dimer: the gas-phase protonation of the neutral dimer that evaporates in advance and the time-lag evaporation of the protonated dimer produced in the subsurface. This methodology is a brand-new way to explore the microstructures and the electron-driven chemical reactions in different local regions of the liquid-vapor interface.

Latest trends in structure based drug design with protein targets

Structure based drug designing is an important endeavor in the field of structural bioinformatics. Previously the entire process was dependent on the wet-lab experiments to build libraries of ligand molecules. And the molecules used to be tested to determine their binding efficacies with protein target. However, the entire process is very lengthy and above all highly expensive. With the advent of supercomputers and increasing computational powers, the search process for finding suitable ligand molecules against target proteins have become more streamlined and cost-effective. Now the entire ligand search process is performed in-silico with the help of the techniques of virtual screening, molecular docking simulations and molecular dynamics studies. In the present chapter, a brief overview of the computational techniques involved in structure based drug designing is presented with a special emphasis on the thermodynamic principles behind the molecular interactions.

A model of molecular vector machine of proteins

Based on the analysis of N_iH⋯O_i-4C bond region of the proteins pentafragments using the quantum chemistry representations, a group of 20 vectors of action has been isolated. These vectors of action are reconstructed by a group of 20 side chains of amino acids being the irreducible representations of vectors. As a result, a model of molecular vector machine of proteins (MVM) has been proposed. This machine includes proteins pentafragments, a system of 20 vectors inside dodecahedron coming out of the O_i-4 atom as from the center and tetrahedral α_i-carbon atom with the side chains of amino acids that change as the polypeptide chain grows. A model with the structured canonical set of amino acids based on the principles of antisymmetry was transferred onto the dodecahedron as an MVM element (Karasev et al., 2005; Karasev et al., 2007). The perspective of MVM application for predicting and designing the secondary structure of proteins has been discussed.

The Role of Interfacial Water in Protein-Ligand Binding: Insights from the Indirect Solvent Mediated Potential of Mean Force

Classical density functional theory (DFT) can be used to relate the thermodynamic properties of solutions to the indirect solvent mediated part of the solute-solvent potential of mean force (PMF). Standard, but powerful numerical methods can be used to estimate the solute-solvent PMF from which the indirect part can be extracted. In this work we show how knowledge of the direct and indirect parts of the solute-solvent PMF for water at the interface of a protein receptor can be used to gain insights about how to design tighter binding ligands. As we show, the indirect part of the solute-solvent PMF is equal to the sum of the 1-body (energy + entropy) terms in the inhomogeneous solvation theory (IST) expansion of the solvation free energy. To illustrate the effect of displacing interfacial water molecules with particular direct/indirect PMF signatures on the binding of ligands, we carry out simulations of protein binding with several pairs of congeneric ligands. We show that interfacial water locations that contribute favorably or unfavorably at the 1-body level (energy + entropy) to the solvation free energy of the solute can be targeted as part of the ligand design process. Water locations where the indirect PMF is larger in magnitude provide better targets for displacement when adding a functional group to a ligand core.

Spatially Heterogeneous Surface Water Diffusivity around Structured Protein Surfaces at Equilibrium

Hydration water on the surface of a protein is thought to mediate the thermodynamics of protein-ligand interactions. For hydration water to play a role beyond modulating global protein solubility or stability, the thermodynamic properties of hydration water must reflect on the properties of the heterogeneous protein surface and thus spatially vary over the protein surface. A potent read-out of local variations in thermodynamic properties of hydration water is its equilibrium dynamics spanning picosecond to nanosecond time scales. In this study, we employ Overhauser dynamic nuclear polarization (ODNP) to probe the equilibrium hydration water dynamics at select sites on the surface of Chemotaxis Y (CheY) in dilute solution. ODNP reports on site-specific hydration water dynamics within 5-10 Å of a label tethered to the biomolecular surface on two separate time scales of motion, corresponding to diffusive water (DW) and protein-water coupled motions, referred to as bound water (BW). We find DW dynamics to be highly heterogeneous across the surface of CheY. We identify a significant correlation between DW dynamics and the local hydropathy of the CheY protein surface, empirically determined by molecular dynamics (MD) simulations, and find the more hydrophobic sites to be hydrated with slower diffusing water. Furthermore, we compare the hydration water dynamics on different polypeptides and liposome surfaces and find the DW dynamics on globular proteins to be significantly more heterogeneous than on intrinsically disordered proteins (IDPs), peptides, and liposomes. The heterogeneity in the hydration water dynamics suggests that structured proteins have the capacity to encode information into the surrounding hydration shell.

Protein biomarkers for early detection of diseases: The decisive contribution of combinatorial peptide ligand libraries

The present review deals with biomarker discovery, especially in regard to sample treatment via combinatorial peptide ligand libraries, perhaps the only technique at present allowing deep exploration of biological fluids and tissue extracts in search for low- to very-low-abundance proteins, which could possibly mark the onset of most pathologies. Early-stage biomarkers, in fact, might be the only way to detect the beginning of most diseases thus permitting proper intervention and care. The following cancers are reviewed, with lists of potential biomarkers suggested in various reports: hepatocellular carcinoma, ovarian cancer, breast cancer and pancreatic cancer, together with some other interesting applications. Although panels of proteins have been presented, with robust evidence, as potential early-stage biomarkers in these different pathologies, their approval by FDA as novel biomarkers in routine clinical chemistry settings would require plenty of additional work and efforts from the pharma industry. The science environment in universities could simply not afford such heavy monetary investments.

SIGNIFICANCE

After more than 16years of search for novel biomarkers, to be used in a clinical chemistry set-up, via proteomic analysis (mostly in biological fluids) it was felt a critical review was due. In the present report, though, only papers reporting biomarker discovery via combinatorial peptide ligand libraries are listed and assessed, since this methodology seems to be the most advanced one for digging in depth into low-to very-low-abundance proteins, which might represent important biomarkers for the onset of pathologies. In particular, a large survey has been made for the following diseases, since they appear to have a large incidence on human population and/or represent fatal diseases: ovarian cancer, breast cancer, pancreatic cancer and hepatocellular carcinoma.

Gene set enrichment and topological analyses based on interaction networks in pediatric acute lymphoblastic leukemia

Pediatric acute lymphoblastic leukemia (ALL) accounts for over one-quarter of all pediatric cancers. Interacting genes and proteins within the larger human gene interaction network of the human genome are rarely investigated by studies investigating pediatric ALL. In the present study, interaction networks were constructed using the empirical Bayesian approach and the Search Tool for the Retrieval of Interacting Genes/proteins database, based on the differentially-expressed (DE) genes in pediatric ALL, which were identified using the RankProd package. Enrichment analysis of the interaction network was performed using the network-based methods EnrichNet and PathExpand, which were compared with the traditional expression analysis systematic explored (EASE) method. In total, 398 DE genes were identified in pediatric ALL, and LIF was the most significantly DE gene. The co-expression network consisted of 272 nodes, which indicated genes and proteins, and 602 edges, which indicated the number of interactions adjacent to the node. Comparison between EASE and PathExpand revealed that PathExpand detected more pathways or processes that were closely associated with pediatric ALL compared with the EASE method. There were 294 nodes and 1,588 edges in the protein-protein interaction network, with the processes of hematopoietic cell lineage and porphyrin metabolism demonstrating a close association with pediatric ALL. Network enrichment analysis based on the PathExpand algorithm was revealed to be more powerful for the analysis of interaction networks in pediatric ALL compared with the EASE method. LIF and MLLT11 were identified as the most significantly DE genes in pediatric ALL. The process of hematopoietic cell lineage was the pathway most significantly associated with pediatric ALL.