Effective approach for calculations of absolute stability of proteins using focused dielectric constants

Effect of nanoarray density on enhanced electron transfer efficiency and analytical sensitivity for electrochemical immunosensors

The fabrication of ordered nanoarray electrode (NAE) using UV imprinting and their application as electrochemical (EC) immunosensor is described in this study. Especially, the influence of the array density factors on the performance of NAE was characterized electrochemically and compared with flat-electrode. Low-density (hole: 200 nm, hole space = 600 nm), medium-density (hole: 200 nm, hole space = 400 nm), and high-density NAE (hole: 200 nm, hole space = 200 nm) which have the same active area were fabricated and their redox cycling was compared with empirical results. We observed that the high-density is the optimum NAE exhibiting the lowest charge transfer resistance and the highest redox cycling performance among all NAEs. Finally, to observe the effect of their EC performance as biosensor, an EC immunoassay was performed using Interleukine-6 (IL-6), and high-density NAE has lowest a low limit of detection (LOD) of 0.45 pg/mL compared with other NAEs (medium-density: 3.91 pg/mL, low-density: 5.87 pg/mL).

Mechanistic study of the transmission pattern of the SARS-CoV-2 omicron variant

The omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) characterized by 30 mutations in its spike protein, has rapidly spread worldwide since November 2021, significantly exacerbating the ongoing COVID-19 pandemic. In order to investigate the relationship between these mutations and the variant's high transmissibility, we conducted a systematic analysis of the mutational effect on spike-angiotensin-converting enzyme-2 (ACE2) interactions and explored the structural/energy correlation of key mutations, utilizing a reliable coarse-grained model. Our study extended beyond the receptor-binding domain (RBD) of spike trimer through comprehensive modeling of the full-length spike trimer rather than just the RBD. Our free-energy calculation revealed that the enhanced binding affinity between the spike protein and the ACE2 receptor is correlated with the increased structural stability of the isolated spike protein, thus explaining the omicron variant's heightened transmissibility. The conclusion was supported by our experimental analyses involving the expression and purification of the full-length spike trimer. Furthermore, the energy decomposition analysis established those electrostatic interactions make major contributions to this effect. We categorized the mutations into four groups and established an analytical framework that can be employed in studying future mutations. Additionally, our calculations rationalized the reduced affinity of the omicron variant towards most available therapeutic neutralizing antibodies, when compared with the wild type. By providing concrete experimental data and offering a solid explanation, this study contributes to a better understanding of the relationship between theories and observations and lays the foundation for future investigations.

Tofacitinib and peficitinib inhibitors of Janus kinase for autoimmune disease treatment: a quantum biochemistry approach

Autoimmune inflammatory diseases, such as rheumatoid arthritis (RA) and ulcerative colitis, are associated with an uncontrolled production of cytokines leading to the pronounced inflammatory response of these disorders. Their therapy is currently focused on the inhibition of cytokine receptors, such as the Janus kinase (JAK) protein family. Tofacitinib and peficitinib are JAK inhibitors that have been recently approved to treat rheumatoid arthritis. In this study, an in-depth analysis was carried out through quantum biochemistry to understand the interactions involved in the complexes formed by JAK1 and tofacitinib or peficitinib. Computational analyses provided new insights into the binding mechanisms between tofacitinib or peficitinib and JAK1. The essential amino acid residues that support the complex are also identified and reported. Additionally, we report new interactions, such as van der Waals; hydrogen bonds; and alkyl, pi-alkyl, and pi-sulfur forces, that stabilize the complexes. The computational results revealed that peficitinib presents a similar affinity to JAK1 compared to tofacitinib based on their interaction energies.

Quantum biochemical analysis of the TtgR regulator and effectors

The recent expansion of multidrug-resistant (MDR) pathogens poses significant challenges in treating healthcare-associated infections. Although antibacterial resistance occurs by numerous mechanisms, active efflux of the drugs is a critical concern. A single species of efflux pump can produce a simultaneous resistance to several drugs. One of the best-studied efflux pumps is the TtgABC: a tripartite resistance-nodulation-division (RND) efflux pump implicated in the intrinsic antibiotic resistance in Pseudomonas putida DOT-T1E. The expression of the TtgABC gene is down-regulated by the HTH-type transcriptional repressor TtgR. In this context, by employing quantum chemistry methods based on the Density Functional Theory (DFT) within the Molecular Fragmentation with Conjugate Caps (MFCC) approach, we investigate the coupling profiles of the transcriptional regulator TtgR in complex with quercetin (QUE), a natural polyphenolic flavonoid, tetracycline (TAC), and chloramphenicol (CLM), two broad-spectrum antimicrobial agents. Our quantum biochemical computational results show the: [i] convergence radius, [ii] total binding energy, [iii] relevance (energetically) of the ligands regions, and [iv] most relevant amino acids residues of the TtgR-QUE/TAC/CLM complexes, pointing out distinctions and similarities among them. These findings improve the understanding of the binding mechanism of effectors and facilitate the development of new chemicals targeting TtgR, helping in the battle against the rise of resistance to antimicrobial drugs. These advances are crucial in the ongoing fight against rising antimicrobial drug resistance, providing hope for a future where healthcare-associated infections can be more beneficially treated.

Investigation of protein-protein interactions and hotspot region on the NSP7-NSP8 binding site in NSP12 of SARS-CoV-2

Background: The RNA-dependent RNA polymerase (RdRp) complex, essential in viral transcription and replication, is a key target for antiviral therapeutics. The core unit of RdRp comprises the nonstructural protein NSP12, with NSP7 and two copies of NSP8 (NSP81 and NSP82) binding to NSP12 to enhance its affinity for viral RNA and polymerase activity. Notably, the interfaces between these subunits are highly conserved, simplifying the design of molecules that can disrupt their interaction. Methods: We conducted a detailed quantum biochemical analysis to characterize the interactions within the NSP12-NSP7, NSP12-NSP81, and NSP12-NSP82 dimers. Our objective was to ascertain the contribution of individual amino acids to these protein-protein interactions, pinpointing hotspot regions crucial for complex stability. Results: The analysis revealed that the NSP12-NSP81 complex possessed the highest total interaction energy (TIE), with 14 pairs of residues demonstrating significant energetic contributions. In contrast, the NSP12-NSP7 complex exhibited substantial interactions in 8 residue pairs, while the NSP12-NSP82 complex had only one pair showing notable interaction. The study highlighted the importance of hydrogen bonds and π-alkyl interactions in maintaining these complexes. Intriguingly, introducing the RNA sequence with Remdesivir into the complex resulted in negligible alterations in both interaction energy and geometric configuration. Conclusion: Our comprehensive analysis of the RdRp complex at the protein-protein interface provides invaluable insights into interaction dynamics and energetics. These findings can guide the design of small molecules or peptide/peptidomimetic ligands to disrupt these critical interactions, offering a strategic pathway for developing effective antiviral drugs.

Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling

In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.

Deciphering Interactions between Potential Inhibitors and the Plasmodium falciparum DHODH Enzyme: A Computational Perspective

Malaria is a parasitic disease that, in its most severe form, can even lead to death. Insect-resistant vectors, insufficiently effective vaccines, and drugs that cannot stop parasitic infestations are making the fight against the disease increasingly difficult. It is known that the enzyme dihydroorotate dehydrogenase (DHODH) is of paramount importance for the synthesis of pyrimidine from the Plasmodium precursor, that is, for its growth and reproduction. Therefore, its blockade can lead to disruption of the parasite's life cycle in the vertebrate host. In this scenario, PfDHODH inhibitors have been considered candidates for a new therapy to stop the parasitic energy source. Given what is known, in this work, we applied molecular fractionation with conjugated caps (MFCC) in the framework of the quantum formalism of density functional theory (DFT) to evaluate the energies of the interactions between the enzyme and the different triazolopyrimidines (DSM483, DMS557, and DSM1), including a complex carrying the mutation C276F. From these results, it was possible to identify the main features of each system, focusing on the wild-type and mutant PfDHODH and examining the major amino acid residues that are part of the four complexes. Our analysis provides new information that can be used to develop new drugs that could prove to be more effective alternatives to present antimalarial drugs.

Quantum biochemistry description of PI3Kα enzyme bound to selective inhibitors

The PI3K class I is composed of four PI3K isoforms that serve as regulatory enzymes governing cellular metabolism, proliferation, and survival. The hyperactivation of PI3Kα is observed in various types of cancer and is linked to poor prognosis. Unfortunately, the development inhibitors selectively targeting one of the isoforms remains challenging, with only few agents in clinical use. The main difficulty arises from the high conservation among residues at the ATP-binding pocket across isoforms, which also serves as target pocket for inhibitors. In this work, molecular dynamics and quantum calculations were performed to investigate the molecular features guiding the binding of selective inhibitors, alpelisib and GDC-0326, into the ATP-binding pocket of PI3Kα. While molecular dynamics allowed crystallographic coordinates to relax, the interaction eergy between each amino acid residues and inhibitors was obtained by combining the Molecular Fractionation with Conjugated Caps scheme with Density Functional Theory calculations. In addition, the atomic charge of ligands in the bound and unbound (free) was calculated. Results indicated that the most relevant residues for the binding of alpelisib are Ile932, Glu859, Val851, Val850, Tyr836, Met922, Ile800, and Ile848, while the most important residues for the binding of GDC-0326 are Ile848, Ile800, Ile932, Gln859, Glu849, and Met922. In addition, residues Trp780, Ile800, Tyr836, Ile848, Gln859 Val850, Val851, Ile932 and Met922 are common hotspots for both inhibitors. Overall, the results from this work contribute to improving the understanding of the molecular mechanisms controlling selectivity and highlight important interactions to be considered during the rational design of new agents.Communicated by Ramaswamy H. Sarma.

Exploring the Spike-hACE 2 Residue-Residue Interaction in Human Coronaviruses SARS-CoV-2, SARS-CoV, and HCoV-NL63

Coronaviruses (CoVs) have been responsible for three major outbreaks since the beginning of the 21st century, and the emergence of the recent COVID-19 pandemic has resulted in considerable efforts to design new therapies against coronaviruses. Thus, it is crucial to understand the structural features of their major proteins related to the virus-host interaction. Several studies have shown that from the seven known CoV human pathogens, three of them use the human Angiotensin-Converting Enzyme 2 (hACE-2) to mediate their host's cell entry: SARS-CoV-2, SARS-CoV, and HCoV-NL63. Therefore, we employed quantum biochemistry techniques within the density function theory (DFT) framework and the molecular fragmentation with conjugate caps (MFCC) approach to analyze the interactions between the hACE-2 and the spike protein-RBD of the three CoVs in order to map the hot-spot residues that form the recognition surface for these complexes and define the similarities and differences in the interaction scenario. The total interaction energy evaluated showed a good agreement with the experimental binding affinity order: SARS-2 > SARS > NL63. A detailed investigation revealed the energetically most relevant regions of hACE-2 and the spike protein for each complex, as well as the key residue-residue interactions. Our results provide valuable information to deeply understand the structural behavior and binding site characteristics that could help to develop antiviral therapeutics that inhibit protein-protein interactions between CoVs S protein and hACE-2.

Dielectric Constant of Aqueous Solutions of Proteins and Organic Polymers from Molecular Dynamics Simulations

The dielectric constant of water/oligomer mixtures, spanning the range from pure water to pure oligomeric melts, is investigated using molecular dynamics (MD) simulations. As prototypical water-soluble organic substances we consider neutral poly-glycine, poly-ethylene glycol and charged monomeric propanic acid. As the water content is reduced, the dielectric constant decreases but does not follow an ideal mixing behavior. The deviations from ideal mixing originate primarily in the non-linear relation between the oligomer mass fraction and collective polarization effects. We find that the dielectric constant is dominated by water polarization, even if the oligomer mass fraction exceeds 50%. By a double extrapolation of the MD simulation results to the limit of vanishing water fraction and to the limit of infinite oligomeric chain length, we estimate the orientational contribution to the dielectric constant of the pure polymeric melts. By this procedure, we obtain ε = 17 {plus minus} 2 for polyglycine and ε = 1 {plus minus} 0.3 for polyethylene glycol. The large difference is rationalized by polarization correlations of glycine units. Interestingly, we find constant temperature simulations to outperform replica exchange simulations in terms of equilibration speed.

In silico analysis of energy interactions between nociceptin/orfanin FQ receptor and two antagonists with potential antidepressive action

This study addresses the binding energies of NOPR-ligand complexes and presents the main amino acid residues involved in the interaction between these complexes.

Quantum Biochemical Investigation of Lys49-PLA2 from Bothrops moojeni

Envenomation via snakebites occurs largely in areas where it is harder to access the hospital. Its mortality rate and sequelae acquired by the survivors symbolize a big challenge for antivenom therapy. In particular, the homologous phospholipase A₂ (Lys49-PLA₂) proteins can induce myonecrosis and are not effectively neutralized by current treatments. Thus, by taking advantage of crystallographic structures of Bothrops moojeni Lys49-PLA₂ complexed with VRD (varespladib) and AIN (aspirin), a quantum biochemistry study based on the molecular fractionation with conjugate cap scheme within the density functional theory formalism is performed to unveil these complexes' detailed interaction energies. The calculations revealed that important interactions between ligands and the Lys49-PLA₂ pocket could occur up to a pocket radius of r = 6.5 (5.0 Å) for VRD (AIN), with the total interaction energy of the VRD ligand being higher than that of the AIN ligand, which is well-correlated with the experimental binding affinity. Furthermore, we have identified the role played by the amino acids LYS0069, LYS0049, LEU0005, ILE0009, CYS0029, GLY0030, HIS0048, PRO0018, ALA0019, CYS0045, TYR0052, TYR0022, PRO0125*, and PHE0126* (LYS0069, LYS0049, GLY0032, LEU0002, and LEU0005) in the VRD↔Lys49-PLA₂ (AIN↔Lys49-PLA₂) complex. Our simulations are a valuable tool to support the big challenge for neutralizing the damages in victims of snakebites.

Simulating the directional translocation of a substrate by the AAA+ motor in the 26S proteasome

This work explored the molecular origin of substrate translocation by the AAA+ motor of the 26S proteasome. This exploration was performed by combining different simulation approaches including calculations of binding free energies, coarse-grained simulations, and considerations of the ATP hydrolysis energy. The simulations were used to construct the free energy landscape for the translocation process. This included the evaluation of the conformational barriers in different translocation steps. Our simulation reveals that the substrate translocation by the AAA+ motor is guided in part by electrostatic interactions. We also validated the experimental observation that bulkier residues in pore loop 1 are responsible for substrate translocation. However, our calculation also reveals that the lysine residues prior to the bulkier residues (conserved along pore loop 1) are also important for the translocation process. We believe that this computational study can help in guiding the ongoing research of the proteasome.

Exploring human porphobilinogen synthase metalloprotein by quantum biochemistry and evolutionary methods

Previous studies have shown the porphobilinogen synthase (PBGS) zinc-binding mechanism and its conservation among the living cells. However, the precise molecular interaction of zinc with the active center of the enzyme is unknown. In particular, quantum chemistry techniques within the density functional theory (DFT) framework have been the key methodology to describe metalloproteins, when one is looking for a compromise between accuracy and computational feasibility. Considering this, we used DFT-based models within the molecular fractionation with conjugate caps scheme to evaluate the binding energy features of zinc interacting with the human PBGS. Besides, phylogenetic and clustering analyses were successfully employed in extracting useful information from protein sequences to identify groups of conserved residues that build the ions-binding site. Our results also report a conservative assessment of the relevant amino acids, as well as the benchmark analysis of the calculation models used. The most relevant intermolecular interactions in Zn2+-PBGS are due to the amino acids CYS0122, CYS0124, CYS0132, ASP0169, SER0168, ARG0221, HIS0131, ASP0120, GLY0133, VAL0121, ARG0209, and ARG0174. Among these residues, we highlighted ASP0120, GLY0133, HIS0131, SER0168, and ARG0209 by co-occurring in all clusters generated by unsupervised clustering analysis. On the other hand, the triple cysteines at 2.5 Å from zinc (CYS0122, CYS0124, and CYS0132) have the highest energy attraction and are absent in the taxa Viridiplantae, Sar, Rhodophyta, and some Bacteria. Additionally, the performance of the DFT-based models shows that the processing time-dependence is more associated with the choice of the basis set than the exchange-correlation functional.

Extension of transferable coarse-grained models to dicationic ionic liquids

In this study, we extended the previously developed coarse-grained (CG) models of mono-cationic ionic liquids (MILs) to di-cationic ILs (DILs). To achieve this purpose, the MD simulations in three different mapping schemes of CG were done and the results of RDF (as a structural property), density (as a volumetric property) and the diffusion coefficient (as a dynamical property) were compared with the corresponding results of the all-atom (AA) simulations for [C₅(mim)₂][BF₄]₂. The previously developed CG models for MILs with the least refinement in parameters were used to extend the CG models for DILs. Since, the first mapping scheme of the CG model showed the best agreement with the results of the AA simulations for the three mentioned studied properties, this scheme was selected to simulate DILs using the CG model. The transferability of the selected CG model to DILs was investigated by comparing the different volumetric, structural and dynamical properties of [C_n(mim)₂][BF₄]₂ (with n = 3, 6, 9, and 12) obtained from the CG model with those obtained using the corresponding atomistic simulations at different thermodynamic state points. The average deviation for the densities of the CG model with respect to the AA results is less than 2%. Furthermore, in both CG and AA models, the densities and isobaric expansion coefficients decrease with increasing temperature and linkage alkyl chain. The structural properties of the studied DILs, i.e. RDFs, nano segregation of domains, heterogeneity order parameters (HOPs) and angle distributions showed good agreements between the results of the CG and AA models. The CG-based calculated diffusion coefficients of the studied DILs at different temperatures showed that this model leads to faster dynamics with respect to the AA model due to the sacrifice of some degrees of freedom in this model. However, the trend of increasing diffusion coefficients with increasing temperature and linkage alkyl chain length is the same in both CG and AA models. Also, there are good agreements between the results of these two models for other dynamical properties, i.e. electrical conductivity, transference numbers and non-Gaussian parameter with increasing linkage alkyl chain and at various temperatures.

Selective Stabilization of Aspartic Acid Protonation State within a Given Protein Conformation Occurs via Specific "Molecular Association"

Proteins involved in proton-/electron-transfer processes often possess "functional" aspartates/aspartic acids (Asp) with variable protonation states. The mechanism of Asp protonation-deprotonation within proteins is unclear. Two questions were asked-the possible types of determinants responsible for Asp protonation-deprotonation and the spatial arrangements of the determinants leading to selective stabilization. The questions were analyzed using nine different solvent models, which scanned the complete protein dielectric range, and four protein models, which illustrated the spatial arrangements around Asp, termed as "molecular association". The methods employed were quantum chemical calculations and constant pH simulations. The types of the determinants identified were charge-charge interaction, H bonding, dipole-π interaction, extended electronic conjugation, dielectric effect, and solvent accessibility. All solvent-exposed Asp [buried fraction (BF) less than 0.5] were aspartates, and buried Asp were either aspartic acids or aspartates, each having a different "molecular association". The exposed aspartates were stabilized via a H-bonding network with bulk water, buried aspartates via salt bridge or, minimum, two intramolecular H bonds, and buried aspartic acids via, minimum, one intramolecular H bond. An "acid-alcohol pair" (involving Ser/Thr/Tyr) was a common determinant to any "functional" buried aspartate/aspartic acid. Higher energy "molecular associations" observed within proteins compared to those within water, presumably, indicated easy molecular restructuring and alteration of the Asp protonation states during a protein-mediated proton/electron transfer.

The urokinase plasminogen activator binding to its receptor: a quantum biochemistry description within an in/homogeneous dielectric function framework with application to uPA–uPAR peptide inhibitors

DFT calculations using the MFCC fragment-based model considering a spatial-dependent dielectric function based on the Poisson–Boltzmann approximation were performed to describe the uPA–uPAR interactions.

Intermolecular interactions of cn-716 and acyl-KR-aldehyde dipeptide inhibitors against Zika virus

Structural representation and graphic panel showing the most relevant residues that contribute to the ZIKV NS2B–NS3–ligand complexes.

Ribosomal RNA-Aminoglycoside Hygromycin B Interaction Energy Calculation within a Density Functional Theory Framework

We intend to investigate the drug-binding energy of each nucleotide inside the aminoglycoside hygromycin B (hygB) binding site of 30S ribosomal RNA (rRNA) subunit by using the molecular fractionation with conjugate caps (MFCC) strategy based on the density functional theory (DFT), considering the functional LDA/PWC, OBS, and the dielectric constant parametrization. Aminoglycosides are bactericidal antibiotics that have high affinity to the prokaryotic rRNA, inhibiting the synthesis of proteins by acting on the main stages of the translation mechanism, whereas binding to rRNA 16S, a component of the 30S ribosomal subunit in prokaryotes. The identification of the nucleotides presenting the most negative binding energies allows us to stabilize hygB in a suitable binding pocket of the 30S ribosomal subunit. In addition, it should be highlighted that mutations in these residues may probably lead to resistance to ribosome-targeting antibiotics. Quantum calculations of aminoglycoside hygromycin B-ribosome complex might contribute to further quantum studies with antibiotics like macrolides and other aminoglycosides.

Simulating biological charge transfer: Continuum dielectric theory or molecular dynamics?

We study the thermodynamic parameters of Marcus's theory of charge transfer, the driving forces and the reorganization energies, using two widely applied approaches to bioenergetic problems that seem to be radically different: continuum dielectric theory via a numerical solution of Poisson's equation, and the thermodynamic integration approach based upon classical Newtonian molecular dynamics, as perfomed by Na et al., PCCP 19, 18,938 (2017). With application to a nitrite reductase NrfHA protein heterodimer, we obtain an excellent agreement between the respective driving forces with an r.m.s. deviation of 1.7 kcal/mol, and a lower limit to the reorganization energies. The computational methods turn out to be mutually supportive: molecular dynamics can be used to determine the parameters of a dielectric theory computation, which on the other hand can be used to properly rescale the reorganization energies and partition them into aqueous and protein contributions. In addition, we use the electrostatic approach to study the influence of Ca²⁺ ions on the free energy landscape of charge transfer.

Explaining urokinase type plasminogen activator inhibition by amino-5-hydroxybenzimidazole and two naphthamidine-based compounds through quantum biochemistry

Urokinase plasminogen activator (uPA) is a biomarker and therapeutic target for several cancer types whose inhibition has been shown to slow tumor growth and metastasis.

Development of constant-pH simulation methods in implicit solvent and applications in biomolecular systems

pH is a critical parameter for biological and technological systems directly related with electrical charges. It can give rise to peculiar electrostatic phenomena, which also makes them more challenging. Due to the quantum nature of the process, involving the forming and breaking of chemical bonds, quantum methods should ideally by employed. Nevertheless, due to the very large number of ionizable sites, different macromolecular conformations, salt conditions, and all other charged species, the CPU time cost simply becomes prohibitive for computer simulations, making this a quite complex problem. Simplified methods based on Monte Carlo sampling have been devised and will be reviewed here, highlighting the updated state-of-the-art of this field, advantages, and limitations of different theoretical protocols for biomolecular systems (proteins and nucleic acids). Following a historical perspective, the discussion will be associated with the applications to protein interactions with other proteins, polyelectrolytes, and nanoparticles.

cDNA cloning, molecular modeling and docking calculations of L-type lectins from Swartzia simplex var. grandiflora (Leguminosae, Papilionoideae), a member of the tribe Swartzieae

The genus Swartzia is a member of the tribe Swartzieae, whose genera constitute the living descendants of one of the early branches of the papilionoid legumes. Legume lectins comprise one of the main families of structurally and evolutionarily related carbohydrate-binding proteins of plant origin. However, these proteins have been poorly investigated in Swartzia and to date, only the lectin from S. laevicarpa seeds (SLL) has been purified. Moreover, no sequence information is known from lectins of any member of the tribe Swartzieae. In the present study, partial cDNA sequences encoding L-type lectins were obtained from developing seeds of S. simplex var. grandiflora. The amino acid sequences of the S. simplex grandiflora lectins (SSGLs) were only averagely related to the known primary structures of legume lectins, with sequence identities not greater than 50-52%. The SSGL sequences were more related to amino acid sequences of papilionoid lectins from members of the tribes Sophoreae and Dalbergieae and from the Cladratis and Vataireoid clades, which constitute with other taxa, the first branching lineages of the subfamily Papilionoideae. The three-dimensional structures of 2 representative SSGLs (SSGL-A and SSGL-E) were predicted by homology modeling using templates that exhibit the characteristic β-sandwich fold of the L-type lectins. Molecular docking calculations predicted that SSGL-A is able to interact with D-galactose, N-acetyl-D-galactosamine and α-lactose, whereas SSGL-E is probably a non-functional lectin due to 2 mutations in the carbohydrate-binding site. Using molecular dynamics simulations followed by density functional theory calculations, the binding free energies of the interaction of SSGL-A with GalNAc and α-lactose were estimated as -31.7 and -47.5 kcal/mol, respectively. These findings gave insights about the carbohydrate-binding specificity of SLL, which binds to immobilized lactose but is not retained in a matrix containing D-GalNAc as ligand.

Explaining RANKL inhibition by OPG through quantum biochemistry computations and insights into peptide-design for the treatment of osteoporosis

Quantum biochemistry computations are applied to precisely describe important protein–protein interactions, providing a basis for the design of inhibitory peptides against osteoporosis.

A quantum chemistry investigation of a potential inhibitory drug against the dengue virus

The total interaction energy of the inhibitor Bz-nKRR-H bound to a serine protease of the dengue virus is mainly due to the action of Asn152, Met49, Tyr161, Asp129 and Gly151 (Met84, Met75, Asp81, Asp79 and Asp80) residues at the NS3 (NS2B) subunit.

Refining the treatment of membrane proteins by coarse-grained models

Obtaining a quantitative description of the membrane proteins stability is crucial for understanding many biological processes. However the advance in this direction has remained a major challenge for both experimental studies and molecular modeling. One of the possible directions is the use of coarse-grained models but such models must be carefully calibrated and validated. Here we use a recent progress in benchmark studies on the energetics of amino acid residue and peptide membrane insertion and membrane protein stability in refining our previously developed coarse-grained model (Vicatos et al., Proteins 2014;82:1168). Our refined model parameters were fitted and/or tested to reproduce water/membrane partitioning energetics of amino acid side chains and a couple of model peptides. This new model provides a reasonable agreement with experiment for absolute folding free energies of several β-barrel membrane proteins as well as effects of point mutations on a relative stability for one of those proteins, OmpLA. The consideration and ranking of different rotameric states for a mutated residue was found to be essential to achieve satisfactory agreement with the reference data.

On the energy components governing molecular recognition in the framework of continuum approaches

Molecular recognition is a process that brings together several biological macromolecules to form a complex and one of the most important characteristics of the process is the binding free energy. Various approaches exist to model the binding free energy, provided the knowledge of the 3D structures of bound and unbound molecules. Among them, continuum approaches are quite appealing due to their computational efficiency while at the same time providing predictions with reasonable accuracy. Here we review recent developments in the field emphasizing on the importance of adopting adequate description of physical processes taking place upon the binding. In particular, we focus on the efforts aiming at capturing some of the atomistic details of the binding phenomena into the continuum framework. When possible, the energy components are reviewed independently of each other. However, it is pointed out that rigorous approaches should consider all energy contributions on the same footage. The two major schemes for utilizing the individual energy components to predict binding affinity are outlined as well.

Quantum molecular modelling of ibuprofen bound to human serum albumin

The total interaction energies of the ibuprofen complexed with FA3/FA4 and FA6 binding sites of human serum albumin are in agreement with the hypothesis that the Sudlow's site II is the main binding pocket for ibuprofen.

Multiscale modeling of biological functions: from enzymes to molecular machines (Nobel Lecture)

A detailed understanding of the action of biological molecules is a pre-requisite for rational advances in health sciences and related fields. Here, the challenge is to move from available structural information to a clear understanding of the underlying function of the system. In light of the complexity of macromolecular complexes, it is essential to use computer simulations to describe how the molecular forces are related to a given function. However, using a full and reliable quantum mechanical representation of large molecular systems has been practically impossible. The solution to this (and related) problems has emerged from the realization that large systems can be spatially divided into a region where the quantum mechanical description is essential (e.g. a region where bonds are being broken), with the remainder of the system being represented on a simpler level by empirical force fields. This idea has been particularly effective in the development of the combined quantum mechanics/molecular mechanics (QM/MM) models. Here, the coupling between the electrostatic effects of the quantum and classical subsystems has been a key to the advances in describing the functions of enzymes and other biological molecules. The same idea of representing complex systems in different resolutions in both time and length scales has been found to be very useful in modeling the action of complex systems. In such cases, starting with coarse grained (CG) representations that were originally found to be very useful in simulating protein folding, and augmenting them with a focus on electrostatic energies, has led to models that are particularly effective in probing the action of molecular machines. The same multiscale idea is likely to play a major role in modeling of even more complex systems, including cells and collections of cells.

The energy landscape of a protein switch

Protein switches are made of highly similar sequences that fold to dramatically different structures. A structural switching system with 31 sequence variants for α and α+β folds has been illustrated experimentally by He et al., Structure, 2012, 20, 283 and is investigated computationally in the present study. Methods to assign a sequence to one of the two folds are reported and analyzed. A fast and accurate protocol to identify the correct fold of the 31 sequences is based on enriching modeled structures using short molecular dynamics (MD) trajectories and scoring these structures with coarse-grained energy functions. We examine five coarse-grained energy functions and illustrate that the Hinds-Levitt potential works the best for this task. We show that enrichment by MD significantly enhances prediction accuracy. Finally, we find that melting temperature correlates well with the energy difference between the two folds (correlation coefficient ∼-0.7). The correlation reduces dramatically (∼0.4) if the absolute energy of the correct fold is considered. Moreover, prediction of melting temperature is sensitive to the structural templates. We emphasize in our analyses the use of native structures as templates since these folds are more readily available from structural biology experiments.

On the nature of the apparent free energy of inserting amino acids into membrane through the translocon

The nature of the biological free energy scale (ΔGapp), obtained from translocon mediated insertion studies, has been a major puzzle and the subject of major controversies. Part of the problem has been the complexity of the insertion process that discouraged workers from considering the feasible kinetics schemes and left the possible impression that ΔGapp presents some simple partition. Here we extend and clarify our recent analysis of the insertion problem using well-defined kinetics schemes and a free energy profile. We point out that although the rate constants of some steps are far from being obvious, it is essential to consider explicitly such schemes in order to advance in analyzing the meaning of ΔGapp. It is then shown that under some equilibrium conditions the kinetics scheme leads to a simple formula that allows one to relate ΔGapp to the actual free energy of partitioning between the water, the membrane, and the translocon. Other options are also considered (including limits with irreversible transitions that can be described by linear free energy relationships (LFERs)). It is concluded that it is unlikely that a kinetics plus thermodynamic based analysis can lead to a result that identifies ΔGapp with the partition between the membrane and the translocon. Thus, we argue that unless such analysis is presented, it is unjustified to assume that ΔGapp corresponds to the membrane translocon equilibrium or to some other arbitrary definition. Furthermore, we point out that the presumption that it is sufficient to just calculate the PMF for going from the translocon (TR) to the membrane and then to assume irreversible diffusive motion to water and for further entrance to the membrane is not a valid analysis. Overall, we point out that it is important to try to relate ΔGapp to a well-defined kinetics scheme (regardless of the complication of the system) in order to determine whether the energies of inserting positively charged residues to the membrane are related to the corresponding ΔGapp. It is also suggested that deviations from our simple formula for equilibrium conditions can help in identifying and analyzing kinetics barriers.

Computational protein design with electrostatic focusing: experimental characterization of a conditionally folded helical domain with a reduced amino acid alphabet

Automated methodologies to design synthetic proteins from first principles use energy computations to estimate the ability of the sequences to adopt a targeted structure. This approach is still far from systematically producing native-like sequences, due, most likely, to inaccuracies when modeling the interactions between the protein and its aqueous environment. This is particularly challenging when engineering small protein domains (with less polar pair interactions than with the solvent). We have re-designed a three-helix bundle, domain B, using a fixed backbone and a four amino acid alphabet. We have enlarged the rotamer library with conformers that increase the weight of electrostatic interactions within the design process without altering the energy function used to compute the folding free energy. Our synthetic sequences show less than 15% similarity to any Swissprot sequence. We have characterized our sequences in different solvents using circular dichroism and nuclear magnetic resonance. The targeted structure achieved is dependent on the solvent used. This method can be readily extended to larger domains. Our method will be useful for the engineering of proteins that become active only in a given solvent and for designing proteins in the context of hydrophobic solvents, an important fraction of the situations in the cell.

On the Dielectric "Constant" of Proteins: Smooth Dielectric Function for Macromolecular Modeling and Its Implementation in DelPhi

Implicit methods for modeling protein electrostatics require dielectric properties of the system to be known, in particular, the value of the dielectric constant of protein. While numerous values of the internal protein dielectric constant were reported in the literature, still there is no consensus of what the optimal value is. Perhaps this is due to the fact that the protein dielectric constant is not a “constant” but is a complex function reflecting the properties of the protein’s structure and sequence. Here, we report an implementation of a Gaussian-based approach to deliver the dielectric constant distribution throughout the protein and surrounding water phase by utilizing the 3D structure of the corresponding macromolecule. In contrast to previous reports, we construct a smooth dielectric function throughout the space of the system to be modeled rather than just constructing a “Gaussian surface” or smoothing molecule–water boundary. Analysis on a large set of proteins shows that (a) the average dielectric constant inside the protein is relatively low, about 6–7, and reaches a value of about 20–30 at the protein’s surface, and (b) high average local dielectric constant values are associated with charged residues while low dielectric constant values are automatically assigned to the regions occupied by hydrophobic residues. In terms of energetics, a benchmarking test was carried out against the experimental pK_a’s of 89 residues in staphylococcal nuclease (SNase) and showed that it results in a much better RMSD (= 1.77 pK) than the corresponding calculations done with a homogeneous high dielectric constant with an optimal value of 10 (RMSD = 2.43 pK).

Predictions from an Ising-like Statistical Mechanical Model on the Dynamic and Thermodynamic Effects of Protein Surface Electrostatics

Charged residues on the surface of a protein are known hot-spots for post-translational modification, protein/ligand-binding, and tuning conformational stabilities. Recent experimental evidence points to the fact that surface electrostatics can also modulate thermodynamic barriers and hence folding mechanisms. To probe for this behavior across different proteins, we develop a novel version of the Wako-Saitô-Muñoz-Eaton (WSME) model in which we include an electrostatic potential term in the energy function while simplifying the treatment of solvation free energy. Both of the energy terms are obtained by quantitatively fitting the model to differential scanning calorimetry (DSC) experiments that carry critical information on the protein partition function. We characterize four sets of structural/functional homologues (HEWL/BLA, CspB, engrailed, α-spectrin) either by fitting the experimental data of a single domain in the homologous set and predicting the conformational behavior of the rest with the same set of parameters or by performing semiblind predictions. The model with the added electrostatic term is able to successfully reproduce the order of thermodynamic stabilities and relaxation rates of most of the homologues. In parallel, we predict diverse conformational features including a wide range of thermodynamic barriers (∼9-40 kJ/mol), broad native ensembles in helical proteins, structured unfolded states and intermediates, rugged folding landscapes, and further provide an independent protein-specific estimate of the folding speed limit at 298 K (1/(7-300 μs)). Our results are evidence that protein surface electrostatics can be tailored to not only engineer stabilities but also folding mechanisms and the ruggedness of the underlying landscape.

Computational protein engineering: bridging the gap between rational design and laboratory evolution

Enzymes are tremendously proficient catalysts, which can be used as extracellular catalysts for a whole host of processes, from chemical synthesis to the generation of novel biofuels. For them to be more amenable to the needs of biotechnology, however, it is often necessary to be able to manipulate their physico-chemical properties in an efficient and streamlined manner, and, ideally, to be able to train them to catalyze completely new reactions. Recent years have seen an explosion of interest in different approaches to achieve this, both in the laboratory, and in silico. There remains, however, a gap between current approaches to computational enzyme design, which have primarily focused on the early stages of the design process, and laboratory evolution, which is an extremely powerful tool for enzyme redesign, but will always be limited by the vastness of sequence space combined with the low frequency for desirable mutations. This review discusses different approaches towards computational enzyme design and demonstrates how combining newly developed screening approaches that can rapidly predict potential mutation “hotspots” with approaches that can quantitatively and reliably dissect the catalytic step can bridge the gap that currently exists between computational enzyme design and laboratory evolution studies.

Dynamic and Thermodynamic Signatures of Native and Non-Native Protein States with Application to the Improvement of Protein Structures

Accurate knowledge of the 3D structural ensemble of proteins is important for understanding of their biological function. We report here the application of microsecond all-atom molecular dynamics (MD) simulations in explicit solvent for the improvement of the quality of low-resolution structures obtained by protein structure prediction (decoys). Seventy MD simulations of ∼1 μs average duration were performed on 13 different protein systems starting from X-ray crystal structures and decoys. Their behavior can be divided into three groups: 22 trajectories converged toward the native state, 27 trajectories displayed a quasi-equilibrium by populating mainly a single non-native free energy basin, and 21 trajectories drifted away from their initial decoy structure transiently visiting multiple free energy minima. To determine whether the native structure can be identified among non-native ensembles, the free energy was determined for each basin by the MM/GBSA method together with the von Mises entropy estimator in dihedral angle space. For the proteins studied here, it is found that the ensembles belonging to free energy basins with the lowest free energies and the longest residence times are most native-like. The results demonstrate that explicit solvent microsecond MD simulations using the latest generation of protein force fields and free energy metrics are sufficiently accurate to permit positive identification of native state ensembles against low-resolution structural models and decoys. The approach can be applied to the direct refinement of predicted or experimental low-resolution protein structures.

Electrostatic origin of the mechanochemical rotary mechanism and the catalytic dwell of F1-ATPase

Understanding the nature of energy transduction in life processes requires a quantitative description of the energetics of the conversion of ATP to ADP by ATPases. Previous attempts to do so have provided an interesting insight but could not account for the rotary mechanism by a nonphenomenological structure/energy description. In particular it has been very challenging to account for the observations of the 80° and 40° rotational substates, without any prior information about such states in the simulation procedure. Here we use a coarse-grained model of F1-ATPase and generate, without the adjustment of phenomenological parameters, a structure-based free energy landscape that reproduces the energetics of the mechanochemical process. It is found that the landscape along the relevant rotary path is determined by the electrostatic free energy and not by steric effects. Furthermore, the generated surface and the corresponding Langevin dynamics simulations identify a hidden conformational barrier that provides a new fundamental interpretation of the catalytic dwell and illuminate the nature of the energy conversion process.

The linear interaction energy method for the prediction of protein stability changes upon mutation

The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. To study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free-energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental ΔΔG values. The method is able to accurately identify destabilizing hot spot mutations; however, it has difficulty in distinguishing between stabilizing and destabilizing mutations because of the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. On the basis of these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance.