Machine Learning and Network Analysis of Molecular Dynamics Trajectories Reveal Two Chains of Red/Ox-specific Residue Interactions in Human Protein Disulfide Isomerase

Acceleration of Reaction Space Projector Analysis Using Combinatorial Optimization: Application to Organic Chemical Reactions

In recent years, automated reaction path search methods have established the concept of a reaction route network. The Reaction Space Projector (ReSPer) visualizes the potential energy hypersurface into a lower-dimensional subspace using principal coordinates. The main time-consuming process in ReSPer is calculating the structural distance matrix, making it impractical for complex organic reaction route networks. We implemented the Alternate Optimization (AO) algorithm, one of the combinatorial optimizations, in ReSPer to reduce computational costs. Evaluations using gold clusters and the Au5 several reaction route networks showed that ReSPer-AO accurately computes distances with lower computational costs. Applying ReSPer-AO to the C5H8O reaction route network clarified dynamic conformation changes in its potential energy landscape. The ReSPer-AO method enables analysis of chemical reactions and dynamic conformations in a low-dimensional reaction space that accurately represents hydrocarbon reaction route networks.

Using residue interaction networks to understand protein function and evolution and to engineer new proteins

Residue interaction networks (RINs) provide graph-based representations of interaction networks within proteins, providing important insight into the factors driving protein structure, function, and stability relationships. There exists a wide range of tools with which to perform RIN analysis, taking into account different types of interactions, input (crystal structures, simulation trajectories, single proteins, or comparative analysis across proteins), as well as formats, including standalone software, web server, and a web application programming interface (API). In particular, the ability to perform comparative RIN analysis across protein families using "metaRINs" provides a valuable tool with which to dissect protein evolution. This, in turn, highlights hotspots to avoid (or target) for in vitro evolutionary studies, providing a powerful framework that can be exploited to engineer new proteins.

AI-integrated network for RNA complex structure and dynamic prediction

RNA complexes are essential components in many cellular processes. The functions of these complexes are linked to their tertiary structures, which are shaped by detailed interface information, such as binding sites, interface contact, and dynamic conformational changes. Network-based approaches have been widely used to analyze RNA complex structures. With their roots in the graph theory, these methods have a long history of providing insight into the static and dynamic properties of RNA molecules. These approaches have been effective in identifying functional binding sites and analyzing the dynamic behavior of RNA complexes. Recently, the advent of artificial intelligence (AI) has brought transformative changes to the field. These technologies have been increasingly applied to studying RNA complex structures, providing new avenues for understanding the complex interactions within RNA complexes. By integrating AI with traditional network analysis methods, researchers can build more accurate models of RNA complex structures, predict their dynamic behaviors, and even design RNA-based inhibitors. In this review, we introduce the integration of network-based methodologies with AI techniques to enhance the understanding of RNA complex structures. We examine how these advanced computational tools can be used to model and analyze the detailed interface information and dynamic behaviors of RNA molecules. Additionally, we explore the potential future directions of how AI-integrated networks can aid in the modeling and analyzing RNA complex structures.

Exploring the Influence of Zinc Ions on the Conformational Stability and Activity of Protein Disulfide Isomerase

The interplay between metal ion binding and the activity of thiol proteins, particularly within the protein disulfide isomerase family, remains an area of active investigation due to the critical role that these proteins play in many vital processes. This research investigates the interaction between recombinant human PDIA1 and zinc ions, focusing on the subsequent implications for PDIA1's conformational stability and enzymatic activity. Employing isothermal titration calorimetry and differential scanning calorimetry, we systematically compared the zinc binding capabilities of both oxidized and reduced forms of PDIA1 and assessed the structural consequences of this interaction. Our results demonstrate that PDIA1 can bind zinc both in reduced and oxidized states, but with significantly different stoichiometry and more pronounced conformational effects in the reduced form of PDIA1. Furthermore, zinc binding was observed to inhibit the catalytic activity of reduced-PDIA1, likely due to induced alterations in its conformation. These findings unveil a potential regulatory mechanism in PDIA1, wherein metal ion binding under reductive conditions modulates its activity. Our study highlights the potential role of zinc in regulating the catalytic function of PDIA1 through conformational modulation, suggesting a nuanced interplay between metal binding and protein stability in the broader context of cellular redox regulation.

Searching for Structure: Characterizing the Protein Conformational Landscape with Clustering-Based Algorithms

The identification and characterization of the main conformations from a protein population are a challenging and inherently high-dimensional problem. Here, we evaluate the performance of the Secondary sTructural Ensembles with machine LeArning (StELa) double-clustering method, which clusters protein structures based on the relationship between the φ and ψ dihedral angles in a protein backbone and the secondary structure of the protein, thus focusing on the local properties of protein structures. The classification of states as vectors composed of the clusters' indices arising naturally from the Ramachandran plot is followed by the hierarchical clustering of the vectors to allow for the identification of the main features of the corresponding free energy landscape (FEL). We compare the performance of StELa with the established root-mean-squared-deviation (RMSD)-based clustering algorithm, which focuses on global properties of protein structures and with Combinatorial Averaged Transient Structure (CATS), the combinatorial averaged transient structure clustering method based on distributions of the φ and ψ dihedral angle coordinates. Using ensembles of conformations from molecular dynamics simulations of intrinsically disordered proteins (IDPs) of various lengths (tau protein fragments) or short fragments from a globular protein, we show that StELa is the clustering method that identifies many of the minima and relevant energy states around the minima from the corresponding FELs. In contrast, the RMSD-based algorithm yields a large number of clusters that usually cover most of the FEL, thus being unable to distinguish between states, while CATS does not sample well the FELs for long IDPs and fragments from globular proteins.

Beyond structural bioinformatics for genomics with dynamics characterization of an expanded KRAS mutational landscape

Current capabilities in genomic sequencing outpace functional interpretations. Our previous work showed that 3D protein structure calculations enhance mechanistic understanding of genetic variation in sequenced tumors and patients with rare diseases. The KRAS GTPase is among the critical genetic factors driving cancer and germline conditions. Because KRAS-altered tumors frequently harbor one of three classic hotspot mutations, nearly all studies have focused on these mutations, leaving significant functional ambiguity across the broader KRAS genomic landscape observed in cancer and non-cancer diseases. Herein, we extend structural bioinformatics with molecular simulations to study an expanded landscape of 86 KRAS mutations. We identify multiple coordinated changes strongly associated with experimentally established KRAS biophysical and biochemical properties. The patterns we observe span hotspot and non-hotspot alterations, which can all dysregulate Switch regions, producing mutation-restricted conformations with different effector binding propensities. We experimentally measured mutation thermostability and identified shared and distinct patterns with simulations. Our results indicate mutation-specific conformations, which show potential for future research into how these alterations reverberate into different molecular and cellular functions. The data we present is not predictable using current genomic tools, demonstrating the added functional information derived from molecular simulations for interpreting human genetic variation.

Molecular Design of FRET Probes Based on Domain Rearrangement of Protein Disulfide Isomerase for Monitoring Intracellular Redox Status

Multidomain proteins can exhibit sophisticated functions based on cooperative interactions and allosteric regulation through spatial rearrangements of the multiple domains. This study explored the potential of using multidomain proteins as a basis for Förster resonance energy transfer (FRET) biosensors, focusing on protein disulfide isomerase (PDI) as a representative example. PDI, a well-studied multidomain protein, undergoes redox-dependent conformational changes, enabling the exposure of a hydrophobic surface extending across the b' and a' domains that serves as the primary binding site for substrates. Taking advantage of the dynamic domain rearrangements of PDI, we developed FRET-based biosensors by fusing the b' and a' domains of thermophilic fungal PDI with fluorescent proteins as the FRET acceptor and donor, respectively. Both experimental and computational approaches were used to characterize FRET efficiency in different redox states. In vitro and in vivo evaluations demonstrated higher FRET efficiency of this biosensor in the oxidized form, reflecting the domain rearrangement and its responsiveness to intracellular redox environments. This novel approach of exploiting redox-dependent domain dynamics in multidomain proteins offers promising opportunities for designing innovative FRET-based biosensors with potential applications in studying cellular redox regulation and beyond.

Beyond Structural Bioinformatics for Genomics with Dynamics Characterization of an Expanded KRAS Mutational Landscape

Current capabilities in genomic sequencing outpace functional interpretations. Our previous work showed that 3D protein structure calculations enhance mechanistic understanding of genetic variation in sequenced tumors and patients with rare diseases. The KRAS GTPase is among the critical genetic factors driving cancer and germline conditions. Because KRAS-altered tumors frequently harbor one of three classic hotspot mutations, nearly all studies have focused on these mutations, leaving significant functional ambiguity across the broader KRAS genomic landscape observed in cancer and non-cancer diseases. Herein, we extend structural bioinformatics with molecular simulations to study an expanded landscape of 86 KRAS mutations. We identify multiple coordinated changes strongly associated with experimentally established KRAS biophysical and biochemical properties. The patterns we observe span hotspot and non-hotspot alterations, which can all dysregulate Switch regions, producing mutation-restricted conformations with different effector binding propensities. We experimentally measured mutation thermostability and identified shared and distinct patterns with simulations. Our results indicate mutation-specific conformations which show potential for future research into how these alterations reverberate into different molecular and cellular functions. The data we present is not predictable using current genomic tools, demonstrating the added functional information derived from molecular simulations for interpreting human genetic variation.

Intermediate-aided allostery mechanism for α-glucosidase by Xanthene-11v as an inhibitor using residue interaction network analysis

Exploring allosteric inhibition and the discovery of new inhibitor binding sites are important studies in protein regulation mechanisms and drug discovery. Structural and network-based analyses of trajectories resulting from molecular dynamics (MD) simulations have been developed to discover protein dynamics, landscape, functions, and allosteric regions. Here, an experimentally suggested non-competitive inhibitor, xanthene-11v, was considered to explore its allosteric inhibition mechanism in α-glucosidase MAL12. Comparative structural and network analyses were applied to eight 250 ns independent MD simulations, four of which were performed in the free state and four of which were performed in ligand-bound forms. Projected two-dimensional free energy landscapes (FEL) were constructed from the probabilistic distribution of conformations along the first two principal components. The post-simulation analyses of the coordinates, side-chain torsion angles, non-covalent interaction networks, network communities, and their centralities were performed on α-glucosidase conformations and the intermediate sub-states. Important communities of residues have been found that connect the allosteric site to the active site. Some of these residues like Thr307, Arg312, TYR344, ILE345, Phe357, Asp406, Val407, Asp408, and Leu436 are the key messengers in the transition pathway between allosteric and active sites. Evaluating the probability distribution of distances between gate residues including Val407 in one community and Phe158, and Pro65 in another community depicted the closure of this gate due to the inhibitor binding. Six macro states of protein were deduced from the topology of FEL and analysis of conformational preference of free and ligand-bound systems to these macro states shows a combination of lock-and-key, conformational selection, and induced fit mechanisms are effective in ligand binding. All these results reveal structural states, allosteric mechanisms, and key players in the inhibition pathway of α-glucosidase by xanthene-11v.

Beyond sequence: Structure-based machine learning

Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.

Molecular dynamics simulations provide structural insight into binding of cyclic dinucleotides to human STING protein

Human stimulator of interferon genes (hSTING) is a signaling adaptor protein that triggers innate immune system by response to cytosolic DNA and second messenger cyclic dinucleotides (CDNs). Natural CDNs contain purine nucleobase with different phosphodiester linkage types (3'-3', 2'-2' or mixed 2'-3'-linkages) and exhibit different binding affinity towards hSTING, ranging from micromolar to nanomolar. High-affinity CDNs are considered as suitable candidates for treatment of chronic hepatitis B and cancer. We have used molecular dynamics simulations to investigate dynamical aspects of binding of natural CDNs (specifically, 2'-2'-cGAMP, 2'-3'-cGAMP, 3'-3'-cGAMP, 3'-3'-c-di-AMP, and 3'-3'-c-di-GMP) with hSTING^wt protein. Our results revealed that CDN/hSTING^wt interactions are controlled by the balance between fluctuations (conformational changes) in the CDN ligand and the protein dynamics. Binding of different CDNs induces different degrees of conformational/dynamics changes in hSTING^wt ligand binding cavity, especially in α₁-helices, the so-called lid region and α₂-tails. The ligand residence time in hSTING^wt protein pocket depends on different contribution of R232 and R238 residues interacting with oxygen atoms of phosphodiester groups in ligand, water distribution around interacting charged centers (in protein residues and ligand) and structural stability of closed conformation state of hSTING^wt protein. These findings may perhaps guide design of new compounds modulating hSTING activity.Communicated by Ramaswamy H. Sarma.

Connecting the dots: A practical evaluation of web-tools for describing protein dynamics as networks

Protein Structure Networks (PSNs) are a well-known mathematical model for estimation and analysis of the three-dimensional protein structure. Investigating the topological architecture of PSNs may help identify the crucial amino acid residues for protein stability and protein-protein interactions, as well as deduce any possible mutational effects. But because proteins go through conformational changes to give rise to essential biological functions, this has to be done dynamically over time. The most effective method to describe protein dynamics is molecular dynamics simulation, with the most popular software programs for manipulating simulations to infer interaction networks being RING, MD-TASK, and NAPS. Here, we compare the computational approaches used by these three tools-all of which are accessible as web servers-to understand the pathogenicity of missense mutations and talk about their potential applications as well as their advantages and disadvantages.

De novo coding variants in the AGO1 gene cause a neurodevelopmental disorder with intellectual disability

BACKGROUND

High-impact pathogenic variants in more than a thousand genes are involved in Mendelian forms of neurodevelopmental disorders (NDD).

METHODS

This study describes the molecular and clinical characterisation of 28 probands with NDD harbouring heterozygous AGO1 coding variants, occurring de novo for all those whose transmission could have been verified (26/28).

RESULTS

A total of 15 unique variants leading to amino acid changes or deletions were identified: 12 missense variants, two in-frame deletions of one codon, and one canonical splice variant leading to a deletion of two amino acid residues. Recurrently identified variants were present in several unrelated individuals: p.(Phe180del), p.(Leu190Pro), p.(Leu190Arg), p.(Gly199Ser), p.(Val254Ile) and p.(Glu376del). AGO1 encodes the Argonaute 1 protein, which functions in gene-silencing pathways mediated by small non-coding RNAs. Three-dimensional protein structure predictions suggest that these variants might alter the flexibility of the AGO1 linker domains, which likely would impair its function in mRNA processing. Affected individuals present with intellectual disability of varying severity, as well as speech and motor delay, autistic behaviour and additional behavioural manifestations.

CONCLUSION

Our study establishes that de novo coding variants in AGO1 are involved in a novel monogenic form of NDD, highly similar to the recently reported AGO2-related NDD.

RING 3.0: fast generation of probabilistic residue interaction networks from structural ensembles

Residue interaction networks (RINs) are used to represent residue contacts in protein structures. Thanks to the advances in network theory, RINs have been proved effective as an alternative to coordinate data in the analysis of complex systems. The RING server calculates high quality and reliable non-covalent molecular interactions based on geometrical parameters. Here, we present the new RING 3.0 version extending the previous functionality in several ways. The underlying software library has been re-engineered to improve speed by an order of magnitude. RING now also supports the mmCIF format and provides typed interactions for the entire PDB chemical component dictionary, including nucleic acids. Moreover, RING now employs probabilistic graphs, where multiple conformations (e.g. NMR or molecular dynamics ensembles) are mapped as weighted edges, opening up new ways to analyze structural data. The web interface has been expanded to include a simultaneous view of the RIN alongside a structure viewer, with both synchronized and clickable. Contact evolution across models (or time) is displayed as a heatmap and can help in the discovery of correlating interaction patterns. The web server, together with an extensive help and tutorial, is available from URL: https://ring.biocomputingup.it/.

Molecular Modeling is an Enabling Approach to Complement and Enhance Channelopathy Research

Hundreds of human membrane proteins form channels that transport necessary ions and compounds, including drugs and metabolites, yet details of their normal function or how function is altered by genetic variants to cause diseases are often unknown. Without this knowledge, researchers are less equipped to develop approaches to diagnose and treat channelopathies. High-resolution computational approaches such as molecular modeling enable researchers to investigate channelopathy protein function, facilitate detailed hypothesis generation, and produce data that is difficult to gather experimentally. Molecular modeling can be tailored to each physiologic context that a protein may act within, some of which may currently be difficult or impossible to assay experimentally. Because many genomic variants are observed in channelopathy proteins from high-throughput sequencing studies, methods with mechanistic value are needed to interpret their effects. The eminent field of structural bioinformatics integrates techniques from multiple disciplines including molecular modeling, computational chemistry, biophysics, and biochemistry, to develop mechanistic hypotheses and enhance the information available for understanding function. Molecular modeling and simulation access 3D and time-dependent information, not currently predictable from sequence. Thus, molecular modeling is valuable for increasing the resolution with which the natural function of protein channels can be investigated, and for interpreting how genomic variants alter them to produce physiologic changes that manifest as channelopathies. © 2022 American Physiological Society. Compr Physiol 12:3141-3166, 2022.

Behavior of Linear and Nonlinear Dimensionality Reduction for Collective Variable Identification of Small Molecule Solution-Phase Reactions

Identifying collective variables (CVs) for chemical reactions is essential to reduce the 3N-dimensional energy landscape into lower dimensional basins and barriers of interest. However, in condensed phase processes, the nonmeaningful motions of bulk solvent often overpower the ability of dimensionality reduction methods to identify correlated motions that underpin collective variables. Yet solvent can play important indirect or direct roles in reactivity, and much can be lost through treatments that remove or dampen solvent motion. This has been amply demonstrated within principal component analysis (PCA), although less is known about the behavior of nonlinear dimensionality reduction methods, e.g., uniform manifold approximation and projection (UMAP), that have become recently utilized. The latter presents an interesting alternative to linear methods though often at the expense of interpretability. This work presents distance-attenuated projection methods of atomic coordinates that facilitate the application of both PCA and UMAP to identify collective variables in the presence of explicit solvent and further the specific identity of solvent molecules that participate in chemical reactions. The performance of both methods is examined in detail for two reactions where the explicit solvent plays very different roles within the collective variables. When applied to raw molecular dynamics data in solution, both PCA and UMAP representations are dominated by bulk solvent motions. On the other hand, when applied to data preprocessed by our attenuated projection methods, both PCA and UMAP identify the appropriate collective variables (though varying sensitivity is observed due to the presence of explicit solvent that results from the projection method). Importantly, this approach allows identification of specific solvent molecules that are relevant to the CVs and their importance.

Assessment of the PETase conformational changes induced by poly(ethylene terephthalate) binding

Recently, a bacterium strain of Ideonella sakaiensis was identified with the uncommon ability to degrade the poly(ethylene terephthalate) (PET). The PETase from I. sakaiensis strain 201-F6 (IsPETase) catalyzes the hydrolysis of PET converting it to mono(2-hydroxyethyl) terephthalic acid (MHET), bis(2-hydroxyethyl)-TPA (BHET), and terephthalic acid (TPA). Despite the potential of this enzyme for mitigation or elimination of environmental contaminants, one of the limitations of the use of IsPETase for PET degradation is the fact that it acts only at moderate temperature due to its low thermal stability. Besides, molecular details of the main interactions of PET in the active site of IsPETase remain unclear. Herein, molecular docking and molecular dynamics (MD) simulations were applied to analyze structural changes of IsPETase induced by PET binding. Results from the essential dynamics revealed that the β1-β2 connecting loop is very flexible. This loop is located far from the active site of IsPETase and we suggest that it can be considered for mutagenesis to increase the thermal stability of IsPETase. The free energy landscape (FEL) demonstrates that the main change in the transition between the unbound to the bound state is associated with the β7-α5 connecting loop, where the catalytic residue Asp206 is located. Overall, the present study provides insights into the molecular binding mechanism of PET into the IsPETase structure and a computational strategy for mapping flexible regions of this enzyme, which can be useful for the engineering of more efficient enzymes for recycling plastic polymers using biological systems.

How the Strain Origin of Zika Virus NS1 Protein Impacts Its Dynamics and Implications to Their Differential Virulence

Viruses can impact and affect human populations in a severe way. The appropriate differentiation among several species or strains of viruses is one of the biggest challenges for virology and infectiology studies. The detection of measurables-quantified discrepancies allows for more accurate clinical diagnoses and treatments for viral diseases. In the present study, we have used a computational approach to explore the dynamical properties of the nonstructural protein 1 from two strains of Zika virus. Our results show that despite a high sequence similarity, the two viral proteins from different origins can exhibit significant dissimilar structural dynamics, which complement their reported differential virulence. The present study opens up new ways in the understanding of the infectivity for these biological entities.

Drug design targeting active posttranslational modification protein isoforms

Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as "Post-translational Modification Inspired Drug Design (PTMI-DD)." PTMI-DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI-DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild-type counterpart, targeting protein-protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms.

A molecular dynamics approach on the Y393C variant of protein disulfide isomerase A1

Human protein disulfide isomerase A1 (PDIA1) shows both catalytic (i.e., oxidoreductase) and non-catalytic (i.e., chaperone) activities and plays a crucial role in the oxidative folding of proteins within the endoplasmic reticulum. PDIA1 dysregulation is a common trait in numerous pathophysiological conditions, including neurodegenerative disorders and cancerous diseases. The 1178A>G mutation of the human PDIA1-encoding gene is a non-synonymous single nucleotide polymorphism detected in patients with Cole-Carpenter syndrome type 1 (CSS1), a particularly rare bone disease. In vitro studies showed that the encoded variant (PDIA1 Y393C) exhibits limited oxidoreductase activity. To gain knowledge on the structure-function relationship, we undertook a molecular dynamics (MD) approach to examine the structural stability of PDIA1 Y393C. Results showed that significant conformational changes are the structural consequence of the amino acid substitution Tyr>Cys at position 393 of the PDIA1 protein. This structure-based study provides further knowledge about the molecular origin of CCS1.

Recognition Dynamics of Cancer Mutations on the ERp57-Tapasin Interface

Down regulation of the major histocompatibility class (MHC) I pathway plays an important role in tumour development, and can be achieved by suppression of HLA expression or mutations in the MHC peptide-binding pocket. The peptide-loading complex (PLC) loads peptides on the MHC-I molecule in a dynamic multi-step assembly process. The effects of cancer variants on ERp57 and tapasin components from the MHC-I pathway is less known, and they could have an impact on antigen presentation. Applying computational approaches, we analysed whether the ERp57-tapasin binding might be altered by missense mutations. The variants H408R(ERp57) and P96L, D100A, G183R(tapasin) at the protein–protein interface improved protein stability (ΔΔG) during the initial screen of 14 different variants. The H408R(ERp57) and P96L(tapasin) variants, located close to disulphide bonds, were further studied by molecular dynamics (MD). Identifying intramolecular a-a’ domain interactions, MD revealed open and closed conformations of ERp57 in the presence and absence of tapasin. In wild-type and mutant ERp57-tapasin complexes, residues Val97, Ser98, Tyr100, Trp405, Gly407(ERp57) and Asn94, Cys95, Arg97, Asp100(tapasin) formed common H-bond interactions. Moreover, comparing the H-bond networks for P96L and H408R with each other, suggests that P96L(tapasin) improved ERp57-tapasin binding more than the H408R(ERp57) mutant. During MD, the C-terminus domain (that binds MHC-I) in tapasin from the ERp57(H408R)-tapasin complex moved away from the PLC, whereas in the ERp57-tapasin(P96L) system was oppositely displaced. These findings can have implications for the function of PLC and, ultimately, for the presentation of MHC-I peptide complex on the tumour cell surface.

Integrated Computational Approaches and Tools forAllosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.

Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design

Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design. However, drug discovery efforts have not focused much on targeting Rab allosteric non-nucleotide binding sites which are subjected to less evolutionary pressures to be conserved, hence are likely to offer subfamily specificity and may be less prone to undesirable off-target interactions and side effects. To discover druggable allosteric binding sites, Rab structural dynamics need to be first incorporated using multiple experimentally and computationally obtained structures. The high-dimensional structural data may necessitate feature extraction methods to identify manageable representative structures for subsequent analyses. We have detailed state-of-the-art computational methods to (i) identify binding sites using data on sequence, shape, energy, etc., (ii) determine the allosteric nature of these binding sites based on structural ensembles, residue networks and correlated motions and (iii) identify small molecule binders through structure- and ligand-based virtual screening. To benefit future studies for targeting Rab allosteric sites, we herein detail a refined workflow comprising multiple available computational methods, which have been successfully used alone or in combinations. This workflow is also applicable for drug discovery efforts targeting other medically important proteins. Depending on the structural dynamics of proteins of interest, researchers can select suitable strategies for allosteric drug discovery and design, from the resources of computational methods and tools enlisted in the workflow.

The investigation of the G-quadruplex aptamer selectivity to Pb2+ ion: a joint molecular dynamics simulation and density functional theory study

The aptamers with the ability to form a G-quadruplex structure can be stable in the presence of some ions. Hence, study of the interactions between such aptamers and ions can be beneficial to determine the highest selective aptamer toward an ion. In this article, molecular dynamics (MD) simulations and quantum mechanics (QM) calculations have been applied to investigate the selectivity of the T30695 aptamer toward Pb²⁺ in comparison with some ions. The Free Energy Landscape (FEL) analysis indicates that Pb²⁺ has remained inside the aptamer during the MD simulation, while the other ions have left it. The Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) binding energies prove that the conformational stability of the aptamer is the highest in the presence of Pb²⁺. According to the compaction parameters, the greatest compressed ion-aptamer complex, and hence, the highest ion-aptamer interaction have been induced in the presence of Pb²⁺. The contact maps clarify the closer contacts between the nucleotides of the aptamer in the presence of Pb²⁺. The density functional theory (DFT) results show that Pb²⁺ forms the most stable complex with the aptamer, which is consistent with the MD results. The QM calculations reveal that the N-H bonds and the O…H distances are the longest and the shortest, respectively, in the presence of Pb²⁺. The obtained results verify that the strongest hydrogen bonds (HBs), and hence, the most compressed aptamer structure are induced by Pb²⁺. Besides, atoms in molecules (AIM) and natural bond orbital (NBO) analyses confirm the results.Communicated by Ramaswamy H. Sarma.

Stability and Conformational Resilience of Protein Disulfide Isomerase

Protein disulfide isomerase (PDI) is a redox-dependent protein with oxidoreductase and chaperone activities. It is a U-shaped protein with an abb'xa' structural organization in which the a and a' domains have CGHC active sites, the b and b' domains are involved with substrate binding, and x is a flexible linker. PDI exhibits substantial flexibility and undergoes cycles of unfolding and refolding in its interaction with cholera toxin, suggesting PDI can regain a folded, functional conformation after exposure to stress conditions. To determine whether this unfolding-refolding cycle is a substrate-induced process or an intrinsic physical property of PDI, we used circular dichroism to examine the structural properties of PDI subjected to thermal denaturation. PDI exhibited remarkable conformational resilience that is linked to its redox status. In the reduced state, PDI exhibited a 54 °C unfolding transition temperature (Tm) and regained 85% of its native structure after nearly complete thermal denaturation. Oxidized PDI had a lower Tm of 48-50 °C and regained 70% of its native conformation after 75% denaturation. Both reduced PDI and oxidized PDI were functional after refolding from these denatured states. Additional studies documented increased stability of a PDI construct lacking the a' domain and decreased thermal stability of a construct lacking the a domain. Furthermore, oxidation of the a domain limited the ability of PDI to refold. The stability and conformational resilience of PDI are thus linked to both redox-dependent and domain-specific effects. These findings document previously unrecognized properties of PDI and provide insight into the physical foundation of its biological function.

Gap Junction Channels of Innexins and Connexins: Relations and Computational Perspectives

Gap junction (GJ) channels in invertebrates have been used to understand cell-to-cell communication in vertebrates. GJs are a common form of intercellular communication channels which connect the cytoplasm of adjacent cells. Dysregulation and structural alteration of the gap junction-mediated communication have been proven to be associated with a myriad of symptoms and tissue-specific pathologies. Animal models relying on the invertebrate nervous system have exposed a relationship between GJs and the formation of electrical synapses during embryogenesis and adulthood. The modulation of GJs as a therapeutic and clinical tool may eventually provide an alternative for treating tissue formation-related diseases and cell propagation. This review concerns the similarities between Hirudo medicinalis innexins and human connexins from nucleotide and protein sequence level perspectives. It also sets forth evidence of computational techniques applied to the study of proteins, sequences, and molecular dynamics. Furthermore, we propose machine learning techniques as a method that could be used to study protein structure, gap junction inhibition, metabolism, and drug development.

Molecular basis of rutin inhibition of protein disulfide isomerase (PDI) by combinedin silicoand experimental methods

Protein disulfide isomerase (PDI) is a founding member of the thiol isomerase family, and is recently found to play critical roles in thrombus formation. The development of effective PDI inhibitors is of great significance, and attracts strong interest. We previously showed that rutin bound directly to PDI and inhibited PDI activities, leading to the suppression of platelet aggregation and fibrin generation in a mouse model. A close analog of rutin, isoquercetin, is currently in advanced phase clinical trials. However, the molecular interaction between rutin and PDI is unknown and is difficult to study by X-ray crystallography due to the weak interaction. Here, we generated a molecular model of PDI:rutin complex by molecular docking and thorough molecular dynamics (MD) simulations. We then validated the complex model through a number of different experimental methods. We mutated the key residues predicted by the model and analyzed the mutants by an optimized isothermal titration calorimetry (ITC) method and a functional assay (insulin reduction assay). The results consistently showed that the PDI residues H354, L355 and E359 are important in the binding of rutin. These residues are next to the canonical major substrate binding site of the b′ domain, and were not conserved across the members of thiol isomerases, explaining the specificity of rutin for PDI among vascular thiol isomerases. Furthermore, the inhibitory activities of three rutin analogues were evaluated using an insulin reduction assay. The results supported that the second sugar ring at the side chain of rutin was not necessary for the binding to PDI. Together, this work provides the structural basis for the inhibitory mechanism of rutin to PDI, and offers a promising strategy for the design of new generation inhibitors with higher binding affinity to PDI for therapeutic applications.

Rutin binds and inhibits PDI at b′x domain, H354 is one of the main binding sites.