Protein Residue Networks from Energetic and Geometric Data: Are They Identical?

Use of caps in the auxiliary basis set formulation of the fragment molecular orbital method

An auxiliary polarization formulation of the fragment molecular orbital (FMO) method is developed, combining a basis set correction computed for capped isolated fragments with a polarization obtained from uncapped fragments. For a set of organic and inorganic test systems, it is shown that the total energy and atomic charges are accurately reproduced with respect to full unfragmented calculations. It is demonstrated that the method is accurate for computing electronic excited states. The developed approach is applied to rank the isomers of chignolin from experimental NMR data (PDB: 1UAO) according to their relative energy. Contributions of polarization and basis set effects to pair interactions between fragments are elucidated.

How E-, L-, and P-Selectins Bind to sLex and PSGL-1: A Quantification of Critical Residue Interactions

Selectins and their ability to interact with specific ligands are a cornerstone in cell communication. Over the last three decades, a considerable wealth of experimental and molecular modeling insights into their structure and modus operandi were gathered. Nonetheless, explaining the role of individual selectin residues on a quantitative level remained elusive, despite its importance in understanding the structure-function relationship in these molecules and designing their inhibitors. This work explores essential interactions of selectin-ligand binding, employing a multiscale approach that combines molecular dynamics, quantum-chemical calculations, and residue interaction network models. Such an approach successfully reproduces most of the experimental findings. It proves to be helpful, with the potential for becoming an established tool for quantitative predictions of residue contribution to the binding of biomolecular complexes. The results empower us to quantify the importance of particular residues and functional groups in the protein-ligand interface and to pinpoint differences in molecular recognition by the three selectins. We show that mutations in the E-, L-, and P-selectins, e.g., different residues in positions 46, 85, 97, and 107, present a crucial difference in how the ligand is engaged. We assess the role of sulfation of tyrosine residues in PSGL-1 and suggest that TyrSO3- in position 51 interacting with Arg85 in P-selectin is a significant factor in the increased affinity of P-selectin to PSGL-1 compared to E- and L-selectins. We propose an original pharmacophore targeting five essential PSGL-binding sites based on the analysis of the selectin···PSGL-1 interactions.

Visualizing the Residue Interaction Landscape of Proteins by Temporal Network Embedding

Characterizing the structural dynamics of proteins with heterogeneous conformational landscapes is crucial to understanding complex biomolecular processes. To this end, dimensionality reduction algorithms are used to produce low-dimensional embeddings of the high-dimensional conformational phase space. However, identifying a compact and informative set of input features for the embedding remains an ongoing challenge. Here, we propose to harness the power of Residue Interaction Networks (RINs) and their centrality measures, established tools to provide a graph theoretical view on molecular structure. Specifically, we combine the closeness centrality, which captures global features of the protein conformation at residue-wise resolution, with EncoderMap, a hybrid neural-network autoencoder/multidimensional-scaling like dimensionality reduction algorithm. We find that the resulting low-dimensional embedding is a meaningful visualization of the residue interaction landscape that resolves structural details of the protein behavior while retaining global interpretability. This feature-based graph embedding of temporal protein graphs makes it possible to apply the general descriptive power of RIN formalisms to the analysis of protein simulations of complex processes such as protein folding and multidomain interactions requiring no protein-specific input. We demonstrate this on simulations of the fast folding protein Trp-Cage and the multidomain signaling protein FAT10. Due to its generality and modularity, the presented approach can easily be transferred to other protein systems.

Molecular Modeling Insights into the Structure and Behavior of Integrins: A Review

Integrins are heterodimeric glycoproteins crucial to the physiology and pathology of many biological functions. As adhesion molecules, they mediate immune cell trafficking, migration, and immunological synapse formation during inflammation and cancer. The recognition of the vital roles of integrins in various diseases revealed their therapeutic potential. Despite the great effort in the last thirty years, up to now, only seven integrin-based drugs have entered the market. Recent progress in deciphering integrin functions, signaling, and interactions with ligands, along with advancement in rational drug design strategies, provide an opportunity to exploit their therapeutic potential and discover novel agents. This review will discuss the molecular modeling methods used in determining integrins' dynamic properties and in providing information toward understanding their properties and function at the atomic level. Then, we will survey the relevant contributions and the current understanding of integrin structure, activation, the binding of essential ligands, and the role of molecular modeling methods in the rational design of antagonists. We will emphasize the role played by molecular modeling methods in progress in these areas and the designing of integrin antagonists.

1,4-Dideoxy-1,4-imino-D- and L-lyxitol-based inhibitors bind to Golgi α-mannosidase II in different protonation forms

The development of effective inhibitors of Golgi α-mannosidase II (GMII, E.C.3.2.1.114) with minimal off-target effects on phylogenetically-related lysosomal α-mannosidase (LMan, E.C.3.2.1.24) is a complex task due to the complicated structural and chemical properties of their active sites. The pKa values (and also protonation forms in some cases) of several ionizable amino acids, such as Asp, Glu, His or Arg of enzymes, can be changed upon the binding of the inhibitor. Moreover, GMII and LMan work under different pH conditions. The pKa calculations on large enzyme-inhibitor complexes and FMO-PIEDA energy decomposition analysis were performed on the structures of selected inhibitors obtained from docking and hybrid QM/MM calculations. Based on the calculations, the roles of the amino group incorporated in the ring of the imino-D-lyxitol inhibitors and some ionizable amino acids of Golgi-type (Asp270-Asp340-Asp341 of Drosophila melanogaster α-mannosidase dGMII) and lysosomal-type enzymes (His209-Asp267-Asp268 of Canavalia ensiformis α-mannosidase, JBMan) were explained in connection with the observed inhibitory properties. The pyrrolidine ring of the imino-D-lyxitols prefers at the active site of dGMII the neutral form while in JBMan the protonated form, whereas that of imino-L-lyxitols prefers the protonation form in both enzymes. The calculations indicate that the binding mechanism of inhibitors to the active-site of α-mannosidases is dependent on the inhibitor structure and could be used to design new selective inhibitors of GMII. A series of novel synthetic N-substituted imino-D-lyxitols were evaluated with four enzymes from the glycoside hydrolase GH38 family (two of Golgi-type, Drosophila melanogaster GMIIb and Caenorhabditis elegans AMAN-2, and two of lysosomal-type, Drosophila melanogaster LManII and Canavalia ensiformis JBMan, enzymes). The most potent structures [N-9-amidinononyl and N-2-(1-naphthyl)ethyl derivatives] inhibited GMIIb (Ki = 40 nM) and AMAN-2 (Ki = 150 nM) with a weak selectivity index (SI) toward Golgi-type enzymes of IC50(LManII)/IC50(GMIIb) = 35 or IC50(JBMan)/IC50(AMAN-2) = 86. On the other hand, weaker micromolar inhibitors, such as N-2-naphthylmethyl or 4-iodobenzyl derivatives [IC50(GMIIb) = 2.4 μM and IC50 (AMAN-2) = 7.6 μM], showed a significant SI in the range from 111 to 812.

The Importance of Charge Transfer and Solvent Screening in the Interactions of Backbones and Functional Groups in Amino Acid Residues and Nucleotides

Quantum mechanical (QM) calculations at the level of density-functional tight-binding are applied to a protein–DNA complex (PDB: 2o8b) consisting of 3763 atoms, averaging 100 snapshots from molecular dynamics simulations. A detailed comparison of QM and force field (Amber) results is presented. It is shown that, when solvent screening is taken into account, the contributions of the backbones are small, and the binding of nucleotides in the double helix is governed by the base–base interactions. On the other hand, the backbones can make a substantial contribution to the binding of amino acid residues to nucleotides and other residues. The effect of charge transfer on the interactions is also analyzed, revealing that the actual charge of nucleotides and amino acid residues can differ by as much as 6 and 8% from the formal integer charge, respectively. The effect of interactions on topological models (protein -residue networks) is elucidated.

Computational Enzyme Design at Zymvol

Directed evolution is the most recognized methodology for enzyme engineering. The main drawback resides in its random nature and in the limited sequence exploration; both require screening of thousands (if not millions) of variants to achieve a target function. Computer-driven approaches can limit laboratorial screening to a few hundred candidates, enabling and accelerating the development of industrial enzymes. In this book chapter, the technology adopted at Zymvol is described. An overview of the current development and future directions in the company is also provided.

PSNtools for standalone and web-based structure network analyses of conformational ensembles

Structure graphs, in which interacting amino acids/nucleotides correspond to linked nodes, represent cutting-edge tools to investigate macromolecular function.

The graph-based approach defined as Protein Structure Network (PSN) was initially implemented in the Wordom software and subsequently in the webPSN server. PSNs are computed either on a molecular dynamics (MD) trajectory (PSN-MD) or on a single structure. In the latter case, information on atomic fluctuations is inferred from the Elastic Network Model-Normal Mode Analysis (ENM-NMA) (PSN-ENM). While Wordom performs both PSN-ENM and PSN-MD analyses but without output post-processing, the webPSN server performs only single-structure PSN-EMN but assisting the user in input setup and output analysis.

Here we release for the first time the standalone software PSNtools, which allows calculation and post-processing of PSN analyses carried out either on single structures or on conformational ensembles. Relevant unique and novel features of PSNtools are either comparisons of two networks or computations of consensus networks on sets of homologous/analogous macromolecular structures or conformational ensembles. Network comparisons and consensus serve to infer differences in functionally different states of the same system or network-based signatures in groups of bio-macromolecules sharing either the same functionality or the same fold.

In addition to the new software, here we release also an updated version of the webPSN server, which allows performing an interactive graphical analysis of PSN-MD, following the upload of the PSNtools output.

PSNtools, the auxiliary binary version of Wordom software, and the WebPSN server are freely available at http://webpsn.hpc.unimo.it/wpsn3.php.

Identification of key stabilizing interactions of amyloid-β oligomers based on fragment molecular orbital calculations on macrocyclic β-hairpin peptides

Analyzing the electronic states and inter-/intra-molecular interactions of amyloid oligomers expand our understanding of the molecular basis of Alzheimer's disease and other amyloid diseases. In the current study, several high-resolution crystal structures of oligomeric assemblies of Aβ-derived peptides have been studied by the ab initio fragment molecular orbital (FMO) method. The FMO method provides comprehensive details of the molecular interactions between the residues of the amyloid oligomers at the quantum mechanical level. Based on the calculations, two sequential aromatic residues (F19 and F20) and negatively charged E22 on the central region of Aβ have been identified as key residues in oligomer stabilization and potential interesting pharmacophores for preventing oligomer formation.

pyProGA-A PyMOL plugin for protein residue network analysis

The field of protein residue network (PRN) research has brought several useful methods and techniques for structural analysis of proteins and protein complexes. Many of these are ripe and ready to be used by the proteomics community outside of the PRN specialists. In this paper we present software which collects an ensemble of (network) methods tailored towards the analysis of protein-protein interactions (PPI) and/or interactions of proteins with ligands of other type, e.g. nucleic acids, oligosaccharides etc. In parallel, we propose the use of the network differential analysis as a method to identify residues mediating key interactions between proteins. We use a model system, to show that in combination with other, already published methods, also included in pyProGA, it can be used to make such predictions. Such extended repertoire of methods allows to cross-check predictions with other methods as well, as we show here. In addition, the possibility to construct PRN models from various kinds of input is so far a unique asset of our code. One can use structural data as defined in PDB files and/or from data on residue pair interaction energies, either from force-field parameters or fragment molecular orbital (FMO) calculations. pyProGA is a free open-source software available from https://gitlab.com/Vlado_S/pyproga.

Computational Methods for Biochemical Simulations Implemented in GAMESS

Computational methods for modeling biochemical processes implemented in GAMESS package are reviewed; in particular, quantum mechanics combined with molecular mechanics (QM/MM), semi-empirical, and fragmentation approaches. A detailed summary of capabilities is provided for the QM/MM implementation in QuanPol program and the fragment molecular orbital (FMO) method. Molecular modeling and visualization packages useful for biochemical simulations with GAMESS are described. GAMESS capabilities with corresponding references are tabulated for reader's convenience.

Partition Analysis for Density-Functional Tight-Binding

High-order charge transfer is incorporated into the fragment molecular orbital (FMO) method using a charge transfer state with fractional charges. This state is used for a partition analysis of properties based on segments that may be different from fragments in FMO. The partition analysis is also formulated for calculations without fragmentation. All development in this work is limited to density-functional tight-binding. The analysis is applied to a water cluster, crambin (PDB: 1CBN), and two complexes of Trp-cage (1L2Y) with ligands. The contributions of functional groups in ligands are obtained, providing useful information for drug discovery.

webPSN v2.0: a webserver to infer fingerprints of structural communication in biomacromolecules

A mixed Protein Structure Network (PSN) and Elastic Network Model-Normal Mode Analysis (ENM-NMA)-based strategy (i.e. PSN-ENM) was developed to investigate structural communication in bio-macromolecules. Protein Structure Graphs (PSGs) are computed on a single structure, whereas information on system dynamics is supplied by ENM-NMA. The approach was implemented in a webserver (webPSN), which was significantly updated herein. The webserver now handles both proteins and nucleic acids and relies on an internal upgradable database of network parameters for ions and small molecules in all PDB structures. Apart from the radical restyle of the server and some changes in the calculation setup, other major novelties concern the possibility to: a) compute the differences in nodes, links, and communication pathways between two structures (i.e. network difference) and b) infer links, hubs, communities, and metapaths from consensus networks computed on a number of structures. These new features are useful to identify commonalties and differences between two different functional states of the same system or structural-communication signatures in homologous or analogous systems. The output analysis relies on 3D-representations, interactive tables and graphs, also available for download. Speed and accuracy make this server suitable to comparatively investigate structural communication in large sets of bio-macromolecular systems. URL: http://webpsn.hpc.unimore.it.

ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding

With the development of low order scaling methods for performing Kohn-Sham density functional theory, it is now possible to perform fully quantum mechanical calculations of systems containing tens of thousands of atoms. However, with an increase in the size of the system treated comes an increase in complexity, making it challenging to analyze such large systems and determine the cause of emergent properties. To address this issue, in this paper, we present a systematic complexity reduction methodology which can break down large systems into their constituent fragments and quantify interfragment interactions. The methodology proposed here requires no a priori information or user interaction, allowing a single workflow to be automatically applied to any system of interest. We apply this approach to a variety of different systems and show how it allows for the derivation of new system descriptors, the design of QM/MM partitioning schemes, and the novel application of graph metrics to molecules and materials.

Analyzing Interactions with the Fragment Molecular Orbital Method

Basic concepts in the analysis of binding using the fragment molecular orbital method are discussed at length: polarization, desolvation, and interaction. The components in the pair interaction energy decomposition analysis are introduced, and the analysis is illustrated for a water dimer and a protein-ligand complex.

Characterizing Protein-Protein Interactions with the Fragment Molecular Orbital Method

Proteins are vital components of living systems, serving as building blocks, molecular machines, enzymes, receptors, ion channels, sensors, and transporters. Protein-protein interactions (PPIs) are a key part of their function. There are more than 645,000 reported disease-relevant PPIs in the human interactome, but drugs have been developed for only 2% of these targets. The advances in PPI-focused drug discovery are highly dependent on the availability of structural data and accurate computational tools for analysis of this data. Quantum mechanical approaches are often too expensive computationally, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect. FMO provides essential information for PPI drug discovery, namely, identification of key interactions formed between residues of two proteins, including their strength (in kcal/mol) and their chemical nature (electrostatic or hydrophobic). In this chapter, we have demonstrated how three different FMO-based approaches (pair interaction energy analysis (PIE analysis), subsystem analysis (SA) and analysis of protein residue networks (PRNs)) have been applied to study PPI in three protein-protein complexes.

How inverting β-1,4-galactosyltransferase-1 can quench a high charge of the by-product UDP3−in catalysis: a QM/MM study of enzymatic reaction with native and UDP-5′ thio galactose substrates

The function of Asp252 in the catalysis of β-1,4-galactosyltransferase-1 may be the stabilization of a high charge of the by-product UDP³⁻by a substrate-assisted proton transfer reaction.

Solvent Screening in Zwitterions Analyzed with the Fragment Molecular Orbital Method

Based on induced solvent charges, a new model of solvent screening is developed in the framework of the fragment molecular orbital combined with the polarizable continuum model. The developed model is applied to analyze interactions in a prototypical zwitterionic system, sodium chloride in water, and it is shown that the large underestimation of the interaction in the original solvent screening based on local charges is successfully corrected. The model is also applied to a complex of the Trp-cage (PDB: 1L2Y ) miniprotein with an anionic ligand, and the physical factors determined protein-ligand binding in solution are unraveled.