RISMiCal: A software package to perform fast RISM/3D-RISM calculations.

Solvent plays an essential role in a variety of chemical, physical, and biological processes that occur in the solution phase. The reference interaction site model (RISM) and its three-dimensional extension (3D-RISM) serve as powerful computational tools for modeling solvation effects in chemical reactions, biological functions, and structure formations. We present the RISM integrated calculator (RISMiCal) program package, which is based on RISM and 3D-RISM theories with fast GPU code. RISMiCal has been developed as an integrated RISM/3D-RISM program that has interfaces with external programs such as Gaussian16, GAMESS, and Tinker. Fast 3D-RISM programs for single- and multi-GPU codes written in CUDA would enhance the availability of these hybrid methods because they require the performance of many computationally expensive 3D-RISM calculations. We expect that our package can be widely applied for chemical and biological processes in solvent. The RISMiCal package is available at https://rismical-dev.github.io.

GHGPR-PPIS: A graph convolutional network for identifying protein-protein interaction site using heat kernel with Generalized PageRank techniques and edge self-attention feature processing block.

Accurately pinpointing protein-protein interaction site (PPIS) on the molecular level is of utmost significance for annotating protein function and comprehending the mechanisms underpinning various diseases. While numerous computational methods for predicting PPIS have emerged, they have indeed mitigated the labor and time constraints associated with traditional experimental methods. However, the predictive accuracy of these methods has yet to reach the desired threshold. In this context, we proposed a groundbreaking graph-based computational model called GHGPR-PPIS. This innovative model leveraged a graph convolutional network using heat kernel (GraphHeat) in conjunction with Generalized PageRank techniques (GHGPR) to predict PPIS. Additionally, building upon the GHGPR framework, we devised an edge self-attention feature processing block, further augmenting the performance of the model. Experimental findings conclusively demonstrated that GHGPR-PPIS surpassed all competing state-of-the-art models when evaluated on the benchmark test set. Impressively, on two distinct independent test sets and a specific protein chain, GHGPR-PPIS consistently demonstrated superior generalization performance and practical applicability compared to the comparative model, AGAT-PPIS. Lastly, leveraging the t-SNE dimensionality reduction algorithm and clustering visualization technique, we delved into an interpretability analysis of the effectiveness of GHGPR-PPIS by meticulously comparing the outputs from different stages of the model.

Fast end-to-end surface interpretation of SARS-CoV-2 variants by differentiable molecular surface interaction fingerprinting method.

Confronting the challenge of persistent mutations of SARS-CoV-2, researchers have turned to deep learning methods to predict the mutated structures of spike proteins and to hypothesize potential changes in their structures and drug efficacies. However, limited works are focused on the surface learning of spike proteins even though their biological functions are usually defined by the geometric and chemical features of 3D molecular surfaces. In addition, the current used geometric deep learning methods are based on mesh representations of proteins to identify potential binding targets for drugs. However, the use of meshes has limitations and is not applicable for many important tasks in molecular biology. To address these limitations, we adopt the differentiable molecular surface interaction fingerprinting (dMaSIF) method which is based on the 3D point clouds and a novel efficient geometric convolutional layer to fast predict the interaction sites on the protein surface. The different binding site patterns for Delta, Omicron and its subvariants are clearly visualized. We find that Delta and Omicron show the similar surface binding patterns while BA.2, BA.2.13, BA.3 and BA.4 present similar ones. BA.4 possesses higher positive interaction site ratio than the others which may account for its higher transmission and infection among humans. In addition, the positive interaction site ratios of BA.2, BA.2.13, BA.3 are higher than Delta and Omicron, which are accordant with their transmission and infection rates. Hopefully our work offers a new effective route to analyze the protein-protein interaction for the SARS-CoV-2 variants.

Predicting Protein Interaction Sites Using PITHIA.

Several proteins work independently, but the majority work together to maintain the functions of the cell. Thus, it is crucial to know the interaction sites that facilitate protein-protein interactions. The development of effective computational methods is essential because experimental methods are expensive and time-consuming. This chapter is a guide to predicting protein interaction sites using the program "PITHIA." First, some installation guides are presented, followed by descriptions of input file formats. Afterward, PITHIA's commands and options are outlined with examples. Moreover, some notes are provided on how to extend PITHIA's installation and usage.

Using PlaPPISite to Predict and Analyze Plant Protein-Protein Interaction Sites.

Proteome-wide characterization of protein-protein interactions (PPIs) is crucial to understand the functional roles of protein machinery within cells systematically. With the accumulation of PPI data in different plants, the interaction details of binary PPIs, such as the three-dimensional (3D) structural contexts of interaction sites/interfaces, are urgently demanded. To meet this requirement, we have developed a comprehensive and easy-to-use database called PlaPPISite ( http://zzdlab.com/plappisite/index.php ) to present interaction details for 13 plant interactomes. Here, we provide a clear guide on how to search and view protein interaction details through the PlaPPISite database. Firstly, the running environment of our database is introduced. Secondly, the input file format is briefly introduced. Moreover, we discussed which information related to interaction sites can be achieved through several examples. In addition, some notes about PlaPPISite are also provided. More importantly, we would like to emphasize the importance of interaction site information in plant systems biology through this user guide of PlaPPISite. In particular, the easily accessible 3D structures of PPIs in the coming post-AlphaFold2 era will definitely boost the application of plant interactome to decipher the molecular mechanisms of many fundamental biological issues.

Silicon inhibits the upward transport of Cd in the first internode of different rice varieties in a Cd stressed farm land.

Silicon spraying on leaves can reduce the accumulation of cadmium (Cd) in rice grain. However, it has been found that not all rice varieties decrease in Cd content after silicon (Si) application. A field study was conducted to check the performance of Si on the accumulation and transport of Cd in four rice varieties. TY390 and YXY2, having 51.5%- 60.6% Cd content of grain was inhibited by foliar Si, were classified as CRS varieties; BXY9978 and YXYLS, having Cd content of grain is nonresponsive with Si, were classified as CNS varieties. The Cd contents were mainly accumulated in stem, especially in the first stem node. While foliar Si reported no changes in the Cd content of first node in four different rice varieties. Comparing the correlation between Si and Cd contents in the above part of the first internode of CRS and CNS, as well as the relative expression of Cd transport genes in the first internode suggested that first internode was the key site to effect Cd transport through Si application, and OsZIP7 is a key Cd transporter protein responsive to Si, leading to different response of Cd transport and accmulation between the CRS and the CNS varieties of rice.

AGAT-PPIS: a novel protein-protein interaction site predictor based on augmented graph attention network with initial residual and identity mapping.

Identifying protein-protein interaction (PPI) site is an important step in understanding biological activity, apprehending pathological mechanism and designing novel drugs. Developing reliable computational methods for predicting PPI site as screening tools contributes to reduce lots of time and expensive costs for conventional experiments, but how to improve the accuracy is still challenging. We propose a PPI site predictor, called Augmented Graph Attention Network Protein-Protein Interacting Site (AGAT-PPIS), based on AGAT with initial residual and identity mapping, in which eight AGAT layers are connected to mine node embedding representation deeply. AGAT is our augmented version of graph attention network, with added edge features. Besides, extra node features and edge features are introduced to provide more structural information and increase the translation and rotation invariance of the model. On the benchmark test set, AGAT-PPIS significantly surpasses the state-of-the-art method by 8% in Accuracy, 17.1% in Precision, 11.8% in F1-score, 15.1% in Matthews Correlation Coefficient (MCC), 8.1% in Area Under the Receiver Operating Characteristic curve (AUROC), 14.5% in Area Under the Precision-Recall curve (AUPRC), respectively.

Improving protein-protein interaction site prediction using deep residual neural network.

Accurate identification of protein-protein interaction (PPI) sites is significantly important for understanding the mechanism of life and developing new drugs. However, it is expensive and time-consuming to identify PPI sites using wet-lab experiments. Developing computational methods is a new road to identify PPI sites, which can accelerate the procedure of PPI-related research. In this study, we propose a novel deep learning-based method (called D-PPIsite) to improve the accuracy of sequence-based PPI site prediction. In D-PPIsite, four discriminative sequence-driven features, i.e., position specific scoring matrix, relative solvent accessibility, position information and physical properties, are employed to feed into a well-designed deep learning module, consisting of convolutional, squeeze and excitation, and fully connected layers, to learn a prediction model. To reduce the risk of a single prediction model getting stuck in local optima, multiple prediction models with different initialization parameters are selected and integrated into one final model using the mean ensemble strategy. Experimental results on five independent testing data sets demonstrate that the proposed D-PPIsite can achieve an average accuracy of 80.2% and precision of 36.9%, covering 53.5% of all PPI sites while achieving the average Matthews correlation coefficient value (0.330) that is significantly higher than most of existing state-of-the-art prediction methods. We implement a new standalone-version predictor for predicting PPI sites, which is freely available at https://github.com/MingDongup/D-PPIsite for academic use.

Correlations in Hard- and Soft-Core Generic Polymer Models.

Generic polymer models capturing the chain connectivity and the non-bonded excluded-volume interactions between polymer segments can be classified into hard- and soft-core models depending on their non-bonded pair potential. Here we compared the correlation effects on the structural and thermodynamic properties of the hard- and soft-core models given by the polymer reference interaction site model (PRISM) theory, and found different behaviors of the soft-core models at large invariant degree of polymerization (IDP) depending on how IDP is varied. We also proposed an efficient numerical approach, which enables us to accurately solve the PRISM theory for chain lengths as large as 106.

EPISOL: A software package with expanded functions to perform 3D-RISM calculations for the solvation of chemical and biological molecules.

Integral equation theory (IET) provides an effective solvation model for chemical and biological systems that balances computational efficiency and accuracy. We present a new software package, the expanded package for IET-based solvation (EPISOL), that performs 3D-reference interaction site model (3D-RISM) calculations to obtain the solvation structure and free energies of solute molecules in different solvents. In EPISOL, we have implemented 22 different closures, multiple free energy functionals, and new variations of 3D-RISM theory, including the recent hydrophobicity-induced density inhomogeneity (HI) theory for hydrophobic solutes and ion-dipole correction (IDC) theory for negatively charged solutes. To speed up the convergence and enhance the stability of the self-consistent iterations, we have introduced several numerical schemes in EPISOL, including a newly developed dynamic mixing approach. We show that these schemes have significantly reduced the failure rate of 3D-RISM calculations compared to AMBER-RISM software. EPISOL consists of both a user-friendly graphic interface and a kernel library that allows users to call its routines and adapt them to other programs. EPISOL is compatible with the force-field and coordinate files from both AMBER and GROMACS simulation packages. Moreover, EPISOL is equipped with an internal memory control to efficiently manage the use of physical memory, making it suitable for performing calculations on large biomolecules. We demonstrate that EPISOL can efficiently and accurately calculate solvation density distributions around various solute molecules (including a protein chaperone consisting of 120,715 atoms) and obtain solvent free energy for a wide range of organic compounds. We expect that EPISOL can be widely applied as a solvation model for chemical and biological systems. EPISOL is available at https://github.com/EPISOLrelease/EPISOL.

Revisiting the Use of Quantum Chemical Calculations in LogPoctanol-water Prediction.

The partition coefficients of drug and drug-like molecules between an aqueous and organic phase are an important property for developing new therapeutics. The predictive power of computational methods is used extensively to predict partition coefficients of molecules. The application of quantum chemical calculations is used to develop methods to develop structure-activity relationship models for such prediction, either based on molecular fragment methods, or via direct calculation of solvation free energy in solvent continuum. The applicability, merits, and shortcomings of these developments are revisited here.

Combination mechanism of the ternary composite based on Fe3O4-chitosan-graphene oxide prepared by solvothermal method.

Magnetic nanohybrid combining chitosan and graphene have demonstrated promising application in environmental remediation. Herein, ternary composite MCG based on Fe3O4, chitosan (CS) and graphene oxide (GO) was facilely prepared via solvothermal method. The as prepared composite was characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), Raman, Brunauer/Emmett/Teller-Barret/Joyner/Halenda (BET-BJH) and thermo gravimetric-differential thermal analysis (TG-DTA). The combination mechanism of MCG was unveiled via employing the hard-soft acid-base (HSAB) theory and spectroscopic investigations including X-ray photoelectron spectroscopy (XPS), Ultraviolet-visible (UV-Vis) and fluorescent emission spectra. Particularly, combination mechanism of MCG was elucidated by the probable site to site interaction of the couplet components in MCG, as follows. (1) CS-Fe3O4. The primary interaction is N(NH2)-Fe(III), electron donates from N to Fe, transforming one half of the amino groups of chitosan into positive N+. (2) GO-CS. Amidation reaction is the primary interaction form, converting the other half of the amino groups of chitosan into -C(O)NH-. (3) GO-Fe3O4. Dominant interactions are those of epoxy, hydroxyl and aromatic ring with Fe(III). Moreover, MCG exhibits fair adsorption performance on divalent heavy metals in six consecutive cycles. These explorations may shed light on the design of efficient adsorbent based on Fe3O4-chitosan-graphene architecture.

Solvent effects in four-component relativistic electronic structure theory based on the reference interaction-site model.

A combined method of the Dirac-Hartree-Fock (DHF) method and the reference interaction-site model (RISM) theory is reported; this is the initial implementation of the coupling of the four-component relativistic electronic structure theory and an integral equation theory of molecular liquids. In the method, the DHF and RISM equations are solved self-consistently, and therefore the electronic structure of the solute, including relativistic effects, and the solvation structure are determined simultaneously. The formulation is constructed based on the variational principle with respect to the Helmholtz energy, and analytic free energy gradients are also derived using the variational property. The method is applied to the iodine ion (I- ), methyl iodide (CH3 I), and hydrogen chalcogenide (H2 X, where X = O-Po) in aqueous solutions, and the electronic structures of the solutes, as well as the solvation free energies and their component analysis, solvent distributions, and solute-solvent interactions, are discussed.

PITHIA: Protein Interaction Site Prediction Using Multiple Sequence Alignments and Attention.

Cellular functions are governed by proteins, and, while some proteins work independently, most work by interacting with other proteins. As a result it is crucially important to know the interaction sites that facilitate the interactions between the proteins. Since the experimental methods are costly and time consuming, it is essential to develop effective computational methods. We present PITHIA, a sequence-based deep learning model for protein interaction site prediction that exploits the combination of multiple sequence alignments and learning attention. We demonstrate that our new model clearly outperforms the state-of-the-art models on a wide range of metrics. In order to provide meaningful comparison, we update existing test datasets with new information regarding interaction site, as well as introduce an additional new testing dataset which resolves the shortcomings of the existing ones.

YAPPIS-Finder: A novel method for protein-protein interaction site predictions.

We describe a multi parametric-approach, YAPPIS-Finder, for predicting the PPI sites on protein surface. A non-redundant database of comprised of 2,265 protein-protein interaction interfaces (PPIIs) involving 4,530 protein-protein interacting partners (PPIPs) and depicting the interaction between protein-chains of experimentally determined PPCs was used in designing the YAPPIS-Finder. Parametric score obtained on analyzing these 4,530 PPIPs with respect to their residue interface propensity, their hydrophobic content, and amount of solvation free energy associated with them provided the basis of YAPPIS-Finder. By applying YAPPIS-Finder on another dataset 4,290 PPIPs from 2,145 PPIIs, the optimal range of the parametric scores and protein-probe van der Waals energy of interaction was determined. Subsequently, taking the optimal range of PPIP parametric scores and threshold for protein-probe van der Waals energy of interaction into the consideration, the YAPPIS-Finder was tested on a blind dataset of 554 protein-chains and it was found predicting 69.67% sites correctly. On predicting only one PPI site on each protein-chain, the YAPPIS-Finder found covering 22.91% of actually sites in the predicted site. Contrary to this, the sites predicted by SPPIDER covered 22.7% of actual sites. However, on predicting two PPI sites for each protein-chain, the percentage coverage of actual sites in the predicted sites by YAPPIS-Finder exceeded two-fold (i.e. 41.81%), thus making the YAPPIS-Finder a better method.

The stereospecific interaction sites and target specificity of cGMP analogs in mouse cortex.

cGMP interactors play a role in several pathologies and may be targets for cGMP analog-based drugs, but the success of targeting depends on the biochemical stereospecificity between the cGMP-analog and the interactor. The stereospecificity between general cGMP analogs-or such that are selectivity-modified to obtain, for example, inhibitory actions on a specific target, like the cGMP-dependent protein kinase-have previously been investigated. However, the importance of stereospecificity for cGMP-analog binding to interactors is not known. We, therefore, applied affinity chromatography on mouse cortex proteins utilizing analogs with cyclic phosphate (8-AET-cGMP, 2-AH-cGMP, 2'-AHC-cGMP) and selectivity-modified analogs with sulfur-containing cyclic phosphorothioates (Rp/Sp-8-AET-cGMPS, Rp/Sp-2'-AHC-cGMPS) immobilized to agaroses. The results illustrate the cGMP analogs' stereospecific binding for PKG, PKA regulatory subunits and PKA catalytic subunits, PDEs, and EPAC2 and the involvement of these in various KEGG pathways. For the seven agaroses, PKG, PKA regulatory subunits, and PKA catalytic subunits were more prone to be enriched by 2-AH-, 8-AET-, Rp-8-AET-, and Sp-8-AET-cGMP, whereas PDEs and EPAC2 were more likely to be enriched by 2-AH-, Rp-2'-AHC-, and Rp-8-AET-cGMP. Our findings help elucidate the stereospecific-binding sites essential for the interaction between individual cGMP analogs and cGMP-binding proteins, as well as the cGMP analogs' target specificity, which are two crucial parameters in drug design.

Properties of the Pt(111)/electrolyte electrochemical interface studied with a hybrid DFT-solvation approach.

Self-consistent modeling of the interface between solid metal electrode and liquid electrolyte is a crucial challenge in computational electrochemistry. In this contribution, we adopt the effective screening medium reference interaction site method (ESM-RISM) to study the charged interface between a Pt(111) surface that is partially covered with chemisorbed oxygen and an aqueous acidic electrolyte. This method proves to be well suited to describe the chemisorption and charging state of the interface at controlled electrode potential. We present an in-depth assessment of the ESM-RISM parameterization and of the importance of computing near-surface water molecules explicitly at the quantum mechanical level. We found that ESM-RISM is able to reproduce some key interface properties, including the peculiar, non-monotonic charging relation of the Pt(111)/electrolyte interface. The comparison with independent theoretical models and explicit simulations of the interface reveals strengths and limitations of ESM-RISM for modeling electrochemical interfaces.

Biomolecular Simulations with the Three-Dimensional Reference Interaction Site Model with the Kovalenko-Hirata Closure Molecular Solvation Theory.

The statistical mechanics-based 3-dimensional reference interaction site model with the Kovalenko-Hirata closure (3D-RISM-KH) molecular solvation theory has proven to be an essential part of a multiscale modeling framework, covering a vast region of molecular simulation techniques. The successful application ranges from the small molecule solvation energy to the bulk phase behavior of polymers, macromolecules, etc. The 3D-RISM-KH successfully predicts and explains the molecular mechanisms of self-assembly and aggregation of proteins and peptides related to neurodegeneration, protein-ligand binding, and structure-function related solvation properties. Upon coupling the 3D-RISM-KH theory with a novel multiple time-step molecular dynamic (MD) of the solute biomolecule stabilized by the optimized isokinetic Nosé-Hoover chain thermostat driven by effective solvation forces obtained from 3D-RISM-KH and extrapolated forward by generalized solvation force extrapolation (GSFE), gigantic outer time-steps up to picoseconds to accurately calculate equilibrium properties were obtained in this new quasidynamics protocol. The multiscale OIN/GSFE/3D-RISM-KH algorithm was implemented in the Amber package and well documented for fully flexible model of alanine dipeptide, miniprotein 1L2Y, and protein G in aqueous solution, with a solvent sampling rate ~150 times faster than a standard MD simulation in explicit water. Further acceleration in computation can be achieved by modifying the extent of solvation layers considered in the calculation, as well as by modifying existing closure relations. This enhanced simulation technique has proven applications in protein-ligand binding energy calculations, ligand/solvent binding site prediction, molecular solvation energy calculations, etc. Applications of the RISM-KH theory in molecular simulation are discussed in this work.

iCircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network.

Circular RNAs (circRNAs) are widely expressed in eukaryotes. The genome-wide interactions between circRNAs and RNA-binding proteins (RBPs) can be probed from cross-linking immunoprecipitation with sequencing data. Therefore, computational methods have been developed for identifying RBP binding sites on circRNAs. Unfortunately, those computational methods often suffer from the low discriminative power of feature representations, numerical instability and poor scalability. To address those limitations, we propose a novel computational method called iCircRBP-DHN using deep hierarchical network for discriminating circRNA-RBP binding sites. The network architecture can be regarded as a deep multi-scale residual network followed by bidirectional gated recurrent units (BiGRUs) with the self-attention mechanism, which can simultaneously extract local and global contextual information. Meanwhile, we propose novel encoding schemes by integrating CircRNA2Vec and the K-tuple nucleotide frequency pattern to represent different degrees of nucleotide dependencies. To validate the effectiveness of our proposed iCircRBP-DHN, we compared its performance with other computational methods on 37 circRNAs datasets and 31 linear RNAs datasets, respectively. The experimental results reveal that iCircRBP-DHN can achieve superior performance over those state-of-the-art algorithms. Moreover, we perform motif analysis on circRNAs bound by those different RBPs, demonstrating that our proposed CircRNA2Vec encoding scheme can be promising. The iCircRBP-DHN method is made available at https://github.com/houzl3416/iCircRBP-DHN.

PlaPPISite: a comprehensive resource for plant protein-protein interaction sites.

BACKGROUND: Protein-protein interactions (PPIs) play very important roles in diverse biological processes. Experimentally validated or predicted PPI data have become increasingly available in diverse plant species. To further explore the biological functions of PPIs, understanding the interaction details of plant PPIs (e.g., the 3D structural contexts of interaction sites) is necessary. By integrating bioinformatics algorithms, interaction details can be annotated at different levels and then compiled into user-friendly databases. In our previous study, we developed AraPPISite, which aimed to provide interaction site information for PPIs in the model plant Arabidopsis thaliana. Considering that the application of AraPPISite is limited to one species, it is very natural that AraPPISite should be evolved into a new database that can provide interaction details of PPIs in multiple plants. DESCRIPTION: PlaPPISite (http://zzdlab.com/plappisite/index.php) is a comprehensive, high-coverage and interaction details-oriented database for 13 plant interactomes. In addition to collecting 121 experimentally verified structures of protein complexes, the complex structures of experimental/predicted PPIs in the 13 plants were also constructed, and the corresponding interaction sites were annotated. For the PPIs whose 3D structures could not be modelled, the associated domain-domain interactions (DDIs) and domain-motif interactions (DMIs) were inferred. To facilitate the reliability assessment of predicted PPIs, the source species of interolog templates, GO annotations, subcellular localizations and gene expression similarities are also provided. JavaScript packages were employed to visualize structures of protein complexes, protein interaction sites and protein interaction networks. We also developed an online tool for homology modelling and protein interaction site annotation of protein complexes. All data contained in PlaPPISite are also freely available on the Download page. CONCLUSION: PlaPPISite provides the plant research community with an easy-to-use and comprehensive data resource for the search and analysis of protein interaction details from the 13 important plant species.