Computational identification of key residues regulating fluorescence emission in a red/green cyanobacteriochrome.

Cyanobacteriochromes (CBCRs) are linear tetrapyrrole bilin-binding photoreceptors of cyanobacteria that exhibit high spectral diversity, gaining attention in optogenetics and bioimaging applications. Several engineering studies on CBCRs were attempted, especially for designing near-infrared (NIR) fluorescent proteins with longer fluorescence wavelengths. However, despite continuous efforts, a key component regulating fluorescence emission property in CBCRs is still poorly understood. As a model system, we focused on red/green CBCR Slr1393g3, from the unicellular cyanobacterium Synechocystis sp. PCC 6803 to engineer Pr to get far-red light-emitting property. Energy profiling and pairwise structural comparison of Slr1393g3 variants effectively reveal the mutations that are critical to the fluorescence changes. H497 seems to play a key role in stabilizing the chromophore environment, especially the α3 helix, while H495, T499, and Q502 are potential key residues determining fluorescence emission peak wavelength. We also found that mutations of α2 and α4 helical regions are closely related to the chromophore binding stability and likely affect fluorescence properties. Taken together, our computational analysis suggests that the fluorescence of Slr1393g3 is mainly controlled by the stabilization of the chromophore binding pocket. The predicted key residues potentially regulating the fluorescence emission property of a red/green CBCR will be advantageous for designing improved NIR fluorescent protein when combined with in vitro molecular evolution approaches.

ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes.

Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the dissociation constant and the Gibbs free energy, they are currently not capable of making stable predictions with high accuracy, in particular for complexes consisting of more than two molecules. In this work, we present ProBAN, a new method for predicting binding affinity in protein-protein complexes based on a deep convolutional neural network. Prediction is carried out for the spatial structures of complexes, presented in the format of a 4D tensor, which includes information about the location of atoms and their abilities to participate in various types of interactions realized in protein-protein and protein-peptide complexes. The effectiveness of the model was assessed both on an internal test data set containing complexes consisting of three or more molecules, as well as on an external test for the PPI-Affinity service. As a result, we managed to achieve the best prediction quality on these data sets among all the analyzed models: on the internal test, Pearson correlation R = 0.6, MAE = 1.60, on the external test, R = 0.55, MAE = 1.75. The open-source code, the trained ProBAN model, and the collected dataset are freely available at the following link https://github.com/EABogdanova/ProBAN.

Interfacial residues in protein-protein complexes are in the eyes of the beholder.

Interactions between proteins are vital in almost all biological processes. The characterization of protein-protein interactions helps us understand the mechanistic basis of biological processes, thereby enabling the manipulation of proteins for biotechnological and clinical purposes. The interface residues of a protein-protein complex are assumed to have the following two properties: (a) they always interact with a residue of a partner protein, which forms the basis for distance-based interface residue identification methods, and (b) they are solvent-exposed in the isolated form of the protein and become buried in the complex form, which forms the basis for Accessible Surface Area (ASA)-based methods. The study interrogates this popular assumption by recognizing interface residues in protein-protein complexes through these two methods. The results show that a few residues are identified uniquely by each method, and the extent of conservation, propensities, and their contribution to the stability of protein-protein interaction varies substantially between these residues. The case study analyses showed that interface residues, unique to distance, participate in crucial interactions that hold the proteins together, whereas the interface residues unique to the ASA method have a potential role in the recognition, dynamics, and specificity of the complex and can also be a hotspot. Overall, the study recommends applying both distance and ASA methods so that some interface residues missed by either method but crucial to the stability, recognition, dynamics, and function of protein-protein complexes are identified in a complementary manner.

Small molecule activation by sila/germa boryne species.

The inability of p-block elements to participate in π-backbonding restricts them from activating small molecules like CO, H2 , and so forth. However, the development of the main group metallomimetics became a new pathway, where the main-group elements like boron can bind and activate small molecules like CO and H2 . The concept of the frustrated Lewis pair, Boron-Boron multiple bonds, and borylene are previously illustrated. Some of these reported classes of boron species can mimic the jobs of the metal complexes. Hence, we have theoretically studied the binding of CO/N2 molecules at B-center of elusive species like sila/germa boryne stabilized by donor base ligands (cAAC)BE(Me)(L), where E  Si, L  cAACMe , NHCMe , PMe3 , E  Ge, L  cAACMe and (NHCMe )BE(Me)(cAACMe )). The substitutional analogues of (cAACR )BSiR1 (cAAC) and E  P, L  cAACMe ) have been studied by density functional theory (DFT), natural bond orbital, QTAIM calculations and energy decomposition analysis (EDA) coupled with natural orbital for chemical valence (NOCV) analyses. The computed bond dissociation energy and inner stability analyses by the EDA-NOCV method showed that the CO molecule can bind at the B-center of the above-mentioned species due to stronger σ-donor ability while binding of N2 has been theoretically predicted to be weak. The energy barrier for the CO binding is estimated to be 13-14 kcal/mol by transition state calculation. The change of partial triple bond character to single bond nature of the BSi bond and the bending of CBSi bond angle of sila-boryne species are the reason for the activation energy. Our study reveals the ability of such species to bind and activate the CO molecule to mimic the transition metal-containing complexes. We have additionally shown that binding of Fe(CO)4 and Ni(CO)3 is feasible at Si-center after binding of CO at the B-center.

Theoretical Insight into Complexation Between Cyclocarbons and C60 Fullerene.

This work conducts a comprehensive theoretical study on the non-covalent complexation between cyclocarbons and C60 fullerene for the first time. The binding energy between cyclocarbons and C60 fullerene is significantly stronger than that between two C18 or two C60 fullerenes, indicating a particularly strong affinity. The cyclocarbons and C60 fullerene can spontaneously assemble into complexes in the gas phase at room temperature, and the hydrophobic effect caused by the solvent environment can promote this binding. The binding strength with C60 fullerene increases almost linearly with the increase of cyclocarbon size, and the C34@C60 dimer exhibits a perfect nano-Saturn structure. As the ring size increases, the angle between the two cyclocarbons of the 2 : 1 trimers formed by cyclocarbons and C60 fullerene gradually decreases. In C60@2 C34 trimer, the fullerene is symmetrically surrounded by two cyclocarbons. The results on the trimers formed by cyclocarbon and C60 fullerenes in a 1 : 2 ratio showed when the cyclocarbon sandwiched between two fullerenes is not quite large, the trimers exhibit an ideal dumbbell-like structure, and the presence of the first fullerene has a significant synergistic effect on the binding of the second one. The cyclocarbon greatly promotes the dimerization of fullerenes, which acted as a "molecular glue".

The Peptide Bond: Resonance Increases Bond Order and Complicates Fragmentation.

The enhancement of the peptide bond order by a resonance in the lone pair of N and the π-bond of CO is analyzed. A decomposition of the bond order in terms of localized molecular orbitals is developed and applied to the peptide bond. A combination of two rotations of hybrid orbitals is proposed to improve the boundary treatment in the fragment molecular orbital method. The developed approach is applied to peptide bonds, and it is found crucial to retain the π orbital in the variational space of both fragments across the boundary. The interaction energies between conventional amino acid residues in Trp-cage (1L2Y) are discussed.

Benchmarking ANI potentials as a rescoring function and screening FDA drugs for SARS-CoV-2 Mpro.

Here, we introduce the use of ANI-ML potentials as a rescoring function in the host-guest interaction in molecular docking. Our results show that the "docking power" of ANI potentials can compete with the current scoring functions at the same level of computational cost. Benchmarking studies on CASF-2016 dataset showed that ANI is ranked in the top 5 scoring functions among the other 34 tested. In particular, the ANI predicted interaction energies when used in conjunction with GOLD-PLP scoring function can boost the top ranked solution to be the closest to the x-ray structure. Rapid and accurate calculation of interaction energies between ligand and protein also enables screening of millions of drug candidates/docking poses. Using a unique protocol in which docking by GOLD-PLP, rescoring by ANI-ML potentials and extensive MD simulations along with end state free energy methods are combined, we have screened FDA approved drugs against the SARS-CoV-2 main protease (Mpro). The top six drug molecules suggested by the consensus of these free energy methods have already been in clinical trials or proposed as potential drug molecules in previous theoretical and experimental studies, approving the validity and the power of accuracy in our screening method.

Control ultrafiltration membrane fouling in Chlorella-laden water treatment by integrated heat-activated peroxydisulfate pre-oxidation and coagulation treatment.

The membrane fouling induced by algal extracellular organic matter (EOM) remain a bottleneck in restricting ultrafiltration (UF) application during harmful algal-water treatment. In current study, the application of heat-activated peroxydisulfate (PMS) and coagulation (Aluminum chlorohydrate, PACI) on membrane fouling behavior during Chlorella-laden water treatment was investigated. The membrane fouling mechanism was analyzed using the extended Derjaguin-Landau-Verwey-Over-beek (XDLVO) theory. The results revealed that separated heat-activated PMS could enhance the filtration flux of EOM at high PMS does >0.2 mM, whereas the membrane fouling was further alleviated by combined heat-activated PMS (0.2-1.0 mM) and PACI (20 mg/L) treatment, especially at low PMS dose. Combined heat-activated PMS and PACI pretreatment could effectively increase the adhesive repulsion between membrane and foulants and reduce the cohesion free energies between organic foulants than those by separated heat-activated PMS treatment, making the initial filtration flux reduced and the cake layer looser. Moreover, the organic foulants of proteins, polysaccharides, and humic-like organics were removed. Cake formation was the major fouling mechanism when EOM was treated with/without separated heat-activated PMS treatment, whereas the membrane fouling mechanism was changed from cake layer formation to pore blocking after combined heat-activated PMS and PACI treatment. Overall, this research provided a feasible method in membrane fouling control during Chlorella -laden water treatment.

Revisiting the Most Stable Structures of the Benzene Dimer.

The benzene dimer (BD) is an archetypal model of π∙∙∙π and C-H∙∙∙π noncovalent interactions as they occur in its cofacial and perpendicular arrangements, respectively. The enthalpic stabilization of the related BD structures has been debated for a long time and is revisited here. The revisit is based on results of computations that apply the coupled-cluster theory with singles, doubles and perturbative triples [CCSD(T)] together with large basis sets and extrapolate results to the complete basis set (CBS) limit in order to accurately characterize the three most important stationary points of the intermolecular interaction energy (ΔE) surface of the BD, which correspond to the tilted T-shaped (TT), fully symmetric T-shaped (FT) and slipped-parallel (SP) structures. In the optimal geometries obtained by searching extensive sets of the CCSD(T)/CBS ΔE data of the TT, FT and SP arrangements, the resulting ΔE values were -11.84, -11.34 and -11.21 kJ/mol, respectively. The intrinsic strength of the intermolecular bonding in these configurations was evaluated by analyzing the distance dependence of the CCSD(T)/CBS ΔE data over wide ranges of intermonomer separations. In this way, regions of the relative distances that favor BD structures with either π∙∙∙π or C-H∙∙∙π interactions were found and discussed in a broader context.

Reliable Dimerization Energies for Modeling of Supramolecular Junctions.

Accurate estimates of intermolecular interaction energy, ΔE, are crucial for modeling the properties of organic electronic materials and many other systems. For a diverse set of 50 dimers comprising up to 50 atoms (Set50-50, with 7 of its members being models of single-stacking junctions), benchmark ΔE data were compiled. They were obtained by the focal-point strategy, which involves computations using the canonical variant of the coupled cluster theory with singles, doubles, and perturbative triples [CCSD(T)] performed while applying a large basis set, along with extrapolations of the respective energy components to the complete basis set (CBS) limit. The resulting ΔE data were used to gauge the performance for the Set50-50 of several density-functional theory (DFT)-based approaches, and of one of the localized variants of the CCSD(T) method. This evaluation revealed that (1) the proposed "silver standard" approach, which employs the localized CCSD(T) method and CBS extrapolations, can be expected to provide accuracy better than two kJ/mol for absolute values of ΔE, and (2) from among the DFT techniques, computationally by far the cheapest approach (termed "ωB97X-3c/vDZP" by its authors) performed remarkably well. These findings are directly applicable in cost-effective yet reliable searches of the potential energy surfaces of noncovalent complexes.

The Pivotal Distinction between Antagonists' and Agonists' Binding into Dopamine D4 Receptor-MD and FMO/PIEDA Studies.

The dopamine D4 receptor (D4R) is a promising therapeutic target in widespread diseases, and the search for novel agonists and antagonists appears to be clinically relevant. The mechanism of binding to the receptor (R) for antagonists and agonists varies. In the present study, we conducted an in-depth computational study, teasing out key similarities and differences in binding modes, complex dynamics, and binding energies for D4R agonists and antagonists. The dynamic network method was applied to investigate the communication paths between the ligand (L) and G-protein binding site (GBS) of human D4R. Finally, the fragment molecular orbitals with pair interaction energy decomposition analysis (FMO/PIEDA) scheme was used to estimate the binding energies of L-R complexes. We found that a strong salt bridge with D3.32 initiates the inhibition of the dopamine D4 receptor. This interaction also occurs in the binding of agonists, but the change in the receptor conformation to the active state starts with interaction with cysteine C3.36. Such a mechanism may arise in the case of agonists unable to form a hydrogen bond with the serine S5.46, considered, so far, to be crucial in the activation of GPCRs. The energy calculations using the FMO/PIEDA method indicate that antagonists show higher residue occupancy of the receptor binding site than agonists, suggesting they could form relatively more stable complexes. Additionally, antagonists were characterized by repulsive interactions with S5.46 distinguishing them from agonists.

Hydrocarbon Transportation in Heterogeneous Shale Pores by Molecular Dynamic Simulation.

Shale oil in China is widely distributed and has enormous resource potential. The pores of shale are at the nanoscale, and traditional research methods encounter difficulty in accurately describing the fluid flow mechanism, which has become a bottleneck restricting the industrial development of shale oil in China. To clarify the distribution and migration laws of fluid microstructure in shale nanopores, we constructed a heterogeneous inorganic composite shale model and explored the fluid behavior in different regions of heterogeneous surfaces. The results revealed the adsorption capacity for alkanes in the quartz region was stronger than that in the illite region. When the aperture was small, solid-liquid interactions dominated; as the aperture increased, the bulk fluid achieved a more uniform and higher flow rate. Under conditions of small aperture/low temperature/low pressure gradient, the quartz region maintained a negative slip boundary. Illite was more hydrophilic than quartz; when the water content was low, water molecules formed a "liquid film" on the illite surface, and the oil flux percentages in the illite and quartz regions were 87% and 99%, respectively. At 50% water content, the adsorbed water in the illite region reached saturation, the quartz region remained unsaturated, and the difference in the oil flux percentage of the two regions decreased. At 70% water content, the adsorbed water in the two regions reached a fully saturated state, and a layered structure of "water-two-phase region-water" was formed in the heterogeneous nanopore. This study is of great significance for understanding the occurrence characteristics and flow mechanism of shale oil within inorganic nanopores.

Machine-learning assisted scheduling optimization and its application in quantum chemical calculations.

Easy and effective usage of computational resources is crucial for scientific calculations, both from the perspectives of timeliness and economic efficiency. This work proposes a bi-level optimization framework to optimize the computational sequences. Machine-learning (ML) assisted static load-balancing, and different dynamic load-balancing algorithms can be integrated. Consequently, the computational and scheduling engine of the ParaEngine is developed to invoke optimized quantum chemical (QC) calculations. Illustrated benchmark calculations include high-throughput drug suit, solvent model, P38 protein, and SARS-CoV-2 systems. The results show that the usage rate of given computational resources for high throughput and large-scale fragmentation QC calculations can primarily profit, and faster accomplishing computational tasks can be expected when employing high-performance computing (HPC) clusters.

Interaction energy of Cl2 and Br2 with H2 O: Exchange, dispersion and density the crucial ingredients.

Modern Density Functional Theory models are now suitable for many molecular and condensed phase studies. The study of noncovalent interactions, a well-known drawback, is no longer an insurmountable obstacle through design and empirical corrections. However, using empirical corrections as in the DFT-D methods might not be an all-in-one solution. This work uses a simple system, X2 -H2 O with X = Cl or Br, with two different interactions, halogen-bonded (XB) and hydrogen-halogen (HX), to investigate the capability of current density functional approximations (DFA) in predicting interaction energies with eight different exchange-correlation functionals. SAPT(DFT) provides, for all the studied cases, better predictions than the widely used supermolecular approach. In addition, the components of the interaction energy suggest where some of the shortcomings originate in each DFA. The analysis of the functionals used confirms that PBE0 and ω-B97X-D have a physically correct behavior. Using SAPT(DFT) and PBE0, and ω-B97X-D, we obtained the interaction energy of Cl2 and Br2 inside different clathrate cages and satisfactorily compared with wavefunction results; hence, the lower and upper limits of this value are defined: Cl2 @512 , -5.3 ± 0.3 kcal/mol; Cl2 @512 62 , -5.5 ± 0.1 kcal/mol; Br2 @512 62 , -7.6 ± 1.0 kcal/mol; Br2 @512 63 , -10.6 ± 1.0 kcal/mol; Br2 @512 64 , -10.9 ± 0.8 kcal/mol.

The performance of ANI-ML potentials for ligand-n(H2 O) interaction energies and estimation of hydration free energies from end-point MD simulations.

Here, we investigate the performance of "Accurate NeurAl networK engINe for Molecular Energies" (ANI), trained on small organic compounds, on bulk systems including non-covalent interactions and applicability to estimate solvation (hydration) free energies using the interaction between the ligand and explicit solvent (water) from single-step MD simulations. The method is adopted from ANI using the Atomic Simulation Environment (ASE) and predicts the non-covalent interaction energies at the accuracy of wb97x/6-31G(d) level by a simple linear scaling for the conformations sampled by molecular dynamics (MD) simulations of ligand-n(H2 O) systems. For the first time, we test ANI potentials' abilities to reproduce solvation free energies using linear interaction energy (LIE) formulism by modifying the original LIE equation. Our results on ~250 different complexes show that the method can be accurate and have a correlation of R2  = 0.88-0.89 (MAE <1.0 kcal/mol) to the experimental solvation free energies, outperforming current end-state methods. Moreover, it is competitive to other conventional free energy methods such as FEP and BAR with 15-20 × fold reduced computational cost.

Stability and bonding of carbon(0)-iron-N2 complexes relevant to nitrogenase co-factor: EDA-NOCV analyses.

The factors/structural features which are responsible for the binding, activation and reduction of N2 to NH3 by FeMoco of nitrogenase have not been completely understood well. Several relevant model complexes by Holland et al. and Peters et al. have been synthesized, characterized and studied by theoretical calculations. For a matter of fact, those complexes are much different than real active N2 -binding Fe-sites of FeMoco, which possesses a central C(4-) ion having an eight valence electrons as an µ6 -bridge. Here, a series of [(S3 C(0))Fe(II/I/0)-N2 ]n- complexes in different charged/spin states containing a coordinated σ- and π-donor C(0)-atom which possesses eight outer shell electrons [carbone, (Ph3 P)2 C(0); Ph3 P→C(0)←PPh3 ] and three S-donor sites (i.e. - S-Ar), have been studied by DFT, QTAIM, and EDA-NOCV calculations. The effect of the weak field ligand on Fe-centres and the subsequent N2 -binding has been studied by EDA-NOCV analysis. The role of the oxidation state of Fe and N2 -binding in different charged and spin states of the complex have been investigated by EDA-NOCV analyses. The intrinsic interaction energies of the Fe-N2 bond are in the range from -42/-35 to -67 kcal/mol in their corresponding ground states. The S3 C(0) donor set is argued here to be closer to the actual coordination environment of one of the six Fe-centres of nitrogenase. In comparison, the captivating model complexes reported by Holland et al. and Peter et al. possess a stronger π-acceptor C-ring (S2 Cring donor, π-C donor) and stronger donor set like CP3 (σ-C donor) ligands, respectively.

Bonding and stability of elusive silaboryne (SiB) and germaboryne (GeB) with donor base ligands.

Stabilizing the exotic chemical species possessing multiple bonds is often extremely challenging due to insufficient orbital overlap, especially involving one heavier element. Bulky aryl groups and/or carbene as ligand have previously stabilized the SiSi, GeGe, and BB triple bonds. Herein, theoretical calculations have been carried out to shed light on the stability and bonding of elusive silaboryne/germaboryne (Si/GeB triple bond) stabilized by donor base ligands ((cAAC)BE(Me)(L); E = Si, L = cAACMe , NHCMe , PMe3 ; E = Ge, L = cAACMe ). The heavier analogues (Sn, Pb) have been further studied for comparison. Additionally, the effects of bulky substituents at the Si and N atoms on the structural parameters and stability of those species have been investigated. Energy decomposition analysis coupled with natural orbital for chemical valence (EDA-NOCV; for Si) showed that cAAC/NHC ligands could stabilize the exotic BSi-Me species more efficiently than PMe3 ligands. The BSi partial triple bond of the corresponding species possesses a mixture of one covalent electron sharing BSi σ-bond and two dative π-bonds (B ← Si, B → Si).

Development and testing of an algorithm for efficient MP2/CCSD(T) energy estimation of molecular clusters with the 2-body approach.

This work reports the development and testing of an automated algorithm for estimating the energies of weakly bound molecular clusters employing correlated theory. Firstly, the monomers and dimers of (homo/hetero) clusters are identified, and the sum of one-body and two-body contributions to correlation energy is calculated. The addition of this contribution to the Hartree-Fock full calculation (FC) energies provides a good estimate of the total energies at Møller-Plesset second-order perturbation theory (MP2)/coupled-cluster method with singles and doubles (CCSD) (T)-level theory using augmented Dunning basis sets. The estimated energies for several test clusters show an excellent agreement with their FC counterparts, with a substantial wall-clock time saving employing off-the-shelf hardware. Furthermore, the complete basis set (CBS) limit for MP2 energy computed using the two-body approach also agrees with its CBS energy with its FC counterpart.

Deconstructing SARS-CoV-2 neutralization: A modular molecular framework for computational design and comparison of antibodies and nanobodies targeting the spike RBD.

Since 2020 the COVID-19 pandemic has led scientists to search for strategies to predict the transmissibility and virulence of new severe acute respiratory syndrome coronavirus 2 variants based on the estimation of the affinity of the spike receptor binding domain (RBD) for the human angiotensin-converting enzyme 2 (ACE2) receptor and/or neutralizing antibodies. In this context, our lab developed a computational pipeline to quickly quantify the free energy of interaction at the spike RBD/ACE2 protein-protein interface, reflecting the incidence trend observed in the transmissibility/virulence of the investigated variants. In this new study, we used our pipeline to estimate the free energy of interaction between the RBD from 10 variants, and 14 antibodies (ab), or 5 nanobodies (nb), highlighting the RBD regions preferentially targeted by the investigated ab/nb. Our structural comparative analysis and interaction energy calculations allowed us to propose the most promising RBD regions to be targeted by future ab/nb to be designed by site-directed mutagenesis of existing high-affinity ab/nb, to increase their affinity for the target RBD region, for preventing spike-RBD/ACE2 interactions and virus entry in host cells. Furthermore, we evaluated the ability of the investigated ab/nb to simultaneously interact with the three RBD located on the surface of the trimeric spike protein, which can alternatively be in up- or down- (all-3-up-, all-3-down-, 1-up-/2-down-, 2-up-/1-down-) conformations.

On the Role of Charge Transfer in Many-Body Non-Covalent Interactions.

Charge transfer is one of the mechanisms involved in non-covalent interactions. In molecular dimers, its contribution to pairwise interaction energies has been studied extensively using a variety of interaction energy decomposition schemes. In polar interactions such as hydrogen bonds, it can contribute ten or several tens of percent of the interaction energy. Less is known about its importance in higher-order interactions in many-body systems, mainly because of the lack of methods applicable to this problem. In this work, we extend our method for the quantification of the charge-transfer energy based on constrained DFT to many-body cases and apply it to model trimers extracted from molecular crystals. Our calculations show that charge transfer can account for a large fraction of the total three-body interaction energy. This also has implications for DFT calculations of many-body interactions in general as it is known that many DFT functionals struggle to describe charge-transfer effects correctly.