A network representation of protein structures: implications for protein stability

Structural and functional insights into β-glucosidase derived from Thermoproteus sp. AZ2

β-glucosidase (BGL) is a pivotal enzyme with broad implications across diverse industrial sectors, demonstrating robust proficiency in catalyzing glycosidic bond hydrolysis, particularly critical in lignocellulosic biomass conversion. This study explored the structural features and enzymatic activity of Thermoproteus sp. AZ2-derived BGL (TsBGL2). The optimum temperature and pH for TsBGL2 were 95 °C and 5.0, respectively. TsBGL2 exhibited strong thermal stability, retaining >95 % of its activity after incubation at 99 °C for 10 h. Three high-resolution crystal structures of TsBGL2 revealed a canonical (α/β)8-barrel catalytic domain and thermostabilization mechanism. Based on the structure of TsBGL2, Δ(473-495) TsBGL2 was constructed, revealing a significant decrease in thermal stability. Collectively, this study provides enzymatic properties and structural analyses of TsBGL2, laying the groundwork for further studies of β-glucosidase.

Exploring Rad51 inhibition mechanisms of B02 and IBR2 and identifying prospective drug candidates for Rad51: A computational investigation

Rad51 recombinase is a crucial mediator in homologous recombination, upregulation of Rad51 expression is associated with adverse prognostic outcomes in various types of cancers, rendering it an attractive therapeutic target. Several inhibitors targeting Rad51 have been developed, but their precise interactions with Rad51 at the molecular level and the specific mechanisms by which they inhibit Rad51 function remain largely unexplored. Herein, we employ atomistic molecular simulations, advanced sampling techniques and computational methodologies to elucidate the mechanisms underlying the inhibitory effects of Rad51 inhibitors B02 and IBR2 on Rad51 protein dynamics. Moreover, we leverage multilevel virtual screening strategies to identify potential Rad51 inhibitors from the ChemBL database, emphasizing the pivotal role of key residues within the inhibitor binding pocket for effective inhibitor-protein interaction. Our findings provide insights into the effects of B02 and IBR2 on the molecular dynamics of Rad51 and the alteration of the residue communication network. At the same time, we identified that Cmp-4 and Cmp-9 exhibit dynamics properties similar to Rad51 inhibitors B02 and IBR2, suggesting their potential as candidate therapeutic agents. Our study provides valuable insights into the inhibitory mechanisms of Rad51 inhibitors, offering important theoretical insights for the future development of drugs targeting the Rad51.

Integrating protein contact networks for the engineering of thermostable lipase A

In the field of industrial biocatalysis, the rapid advancement of enzyme functional evolution necessitates new theories and computational methods to achieve target functions with fewer iterations. This study identified key residues affecting enzyme stability by constructing the protein contact network (PCN) of Lipase A. Comparing the PCNs of the wild-type (WT) and the 6B variant revealed that changes in residue interactions and node properties (e.g., degree and betweenness centrality (BC)) positively impacted stability. Using thresholds for degree and BC, 25 candidate sites were screened, and 11 out of 18 single-point mutation designs improved thermal stability. Mutations were divided into three groups (M1, M2, M3) based on network communities and contributions, followed by iterative combinations. M1, containing five mutations distributed across four communities, increased the melting temperature (Tm) by 14.61 °C, close to the predicted 13.97 °C, demonstrating a linear additive effect. In M2, three new mutations resulted in a non-linear additive effect, with a ΔTm of 17.58 °C (Expected ΔTm = 18.93 °C). In contrast, the three new mutations in M3 destabilized the enzyme (Observed ΔTm = 15.94 °C vs Expected ΔTm = 19.92 °C). Molecular dynamics simulations showed that polar edge nodes enhanced network connectivity, while proline mutations rigidified flexible regions, improving stability. Conversely, M3 mutations disrupted α-helix stability by increasing the dihedral angle fluctuations of residue Y161, might to a stability-activity trade-off. The PCN provides valuable insights for developing efficient and precise design strategies.

Atomistic Profiling of KRAS Interactions with Monobodies and Affimer Proteins Through Ensemble-Based Mutational Scanning Unveils Conserved Residue Networks Linking Cryptic Pockets and Regulating Mechanisms of Binding, Specificity and Allostery

KRAS, a historically "undruggable" oncogenic driver, has eluded targeted therapies due to its lack of accessible binding pockets in its active state. This study investigates the conformational dynamics, binding mechanisms, and allosteric communication networks of KRAS in complexes with monobodies (12D1, 12D5) and affimer proteins (K6, K3, K69) to characterize the binding and allosteric mechanisms and hotspots of KRAS binding. Through molecular dynamics simulations, mutational scanning, binding free energy analysis and network-based analyses, we identified conserved allosteric hotspots that serve as critical nodes for long-range communication in KRAS. Key residues in β-strand 4 (F78, L80, F82), α-helix 3 (I93, H95, Y96), β-strand 5 (V114, N116), and α-helix 5 (Y157, L159, R164) consistently emerged as hotspots across diverse binding partners, forming contiguous networks linking functional regions of KRAS. Notably, β-strand 4 acts as a central hub for propagating conformational changes, while the cryptic allosteric pocket centered around H95/Y96 positions targeted by clinically approved inhibitors was identified as a universal hotspot for both binding and allostery. The study also reveals the interplay between structural rigidity and functional flexibility, where stabilization of one region induces compensatory flexibility in others, reflecting KRAS's adaptability to perturbations. We found that monobodies stabilize the switch II region of KRAS, disrupting coupling between switch I and II regions and leading to enhanced mobility in switch I of KRAS. Similarly, affimer K3 leverages the α3-helix as a hinge point to amplify its effects on KRAS dynamics. Mutational scanning and binding free energy analysis highlighted the energetic drivers of KRAS interactions. revealing key hotspot residues, including H95 and Y96 in the α3 helix, as major contributors to binding affinity and selectivity. Network analysis identified β-strand 4 as a central hub for propagating conformational changes, linking distant functional sites. The predicted allosteric hotspots strongly aligned with experimental data, validating the robustness of the computational approach. Despite distinct binding interfaces, shared hotspots highlight a conserved allosteric infrastructure, reinforcing their universal importance in KRAS signaling. The results of this study can inform rational design of small-molecule inhibitors that mimic the effects of monobodies and affimer proteins, challenging the "undruggable" reputation of KRAS.

Accurate Characterization of the Allosteric Energy Landscapes, Binding Hotspots and Long-Range Communications for KRAS Complexes with Effector Proteins : Integrative Approach Using Microsecond Molecular Dynamics, Deep Mutational Scanning of Binding Energetics and Allosteric Network Modeling

KRAS is a pivotal oncoprotein that regulates cell proliferation and survival through interactions with downstream effectors such as RAF1. Oncogenic mutations in KRAS, including G12V, G13D, and Q61R, drive constitutive activation and hyperactivation of signaling pathways, contributing to cancer progression. Despite significant advances in understanding KRAS biology, the structural and dynamic mechanisms of KRAS binding and allostery by which oncogenic mutations enhance KRAS-RAF1 binding and signaling remain incompletely understood. In this study, we employ microsecond molecular dynamics simulations, Markov State Modeling, mutational scanning and binding free energy calculations together with dynamic network modeling to elucidate the effect of KRAS mutations and characterize the thermodynamic and allosteric drivers and hotspots of KRAS binding and oncogenic activation. Our simulations revealed that oncogenic mutations stabilize the open active conformation of KRAS by differentially modulating the flexibility of the switch I and switch II regions, thereby enhancing RAF1 binding affinity. The G12V mutation rigidifies both switch I and switch II, locking KRAS in a stable, active state. In contrast, the G13D mutation moderately reduces switch I flexibility while increasing switch II dynamics, restoring a balance between stability and flexibility. The Q61R mutation induces a more complex conformational landscape, characterized by the increased switch II flexibility and expansion of functional macrostates, which promotes prolonged RAF1 binding and signaling. Mutational scanning of KRAS-RAF1 complexes identified key binding affinity hotspots, including Y40, E37, D38, and D33, and together with the MM-GBSA analysis revealed the hotspots leverage synergistic electrostatic and hydrophobic binding interactions in stabilizing the KRAS-RAF1 complexes. Network-based analysis of allosteric communication identifies critical KRAS residues (e.g., L6, E37, D57, R97) that mediate long-range interactions between the KRAS core and the RAF1 binding interface. The central β-sheet of KRAS emerges as a hub for transmitting conformational changes, linking distant functional sites and facilitating allosteric regulation. Strikingly, the predicted allosteric hotspots align with experimentally identified allosteric binding hotspots that define the energy landscape of KRAS allostery. This study highlights the power of integrating computational modeling with experimental data to unravel the complex dynamics of KRAS and its mutants. The identification of binding hotspots and allosteric communication routes offers new opportunities for developing targeted therapies to disrupt KRAS-RAF1 interactions and inhibit oncogenic signaling. Our results underscore the potential of computational approaches to guide the design of allosteric inhibitors and mutant-specific therapies for KRAS-driven cancers.

The alteration of structural network upon transient association between proteins studied using graph theory

Proteins such as enzymes perform their function by predominant non-covalent bond interactions between transiently interacting units. There is an impact on the overall structural topology of the protein, albeit transient nature of such interactions, that enable proteins to deactivate or activate. This aspect of the alteration of the structural topology is studied by employing protein structural networks, which are node-edge representative models of protein structure, reported as a robust tool for capturing interactions between residues. Several methods have been optimized to collect meaningful, functionally relevant information by studying alteration of structural networks. In this article, different methods of comparing protein structural networks are employed, along with spectral decomposition of graphs to study the subtle impact of protein-protein interactions. A detailed analysis of the structural network of interacting partners is performed across a dataset of around 900 pairs of bound complexes and corresponding unbound protein structures. The variation in network parameters at, around, and far away from the interface are analyzed. Finally, we present interesting case studies, where an allosteric mechanism of structural impact is understood from communication-path detection methods. The results of this analysis are beneficial in understanding protein stability, for future engineering, and docking studies.

Comparison of structural networks across homologous proteins

Protein sequence determines its structure and function. The indirect relationship between protein function and structure lies deep-rooted in the structural topology that has evolved into performing optimal function. The evolution of structure and its interconnectivity has been conventionally studied by comparing the root means square deviation between protein structures at the backbone level. Two factors that are necessary for the quantitative comparison of non-covalent interactions are (a) explicit inclusion of the coordinates of side-chain atoms and (b) consideration of multiple structures from the conformational landscape to account for structural variability. We have recently addressed these fundamental issues by investigating the alteration of inter-residue interactions across an ensemble of protein structure networks through a graph spectral approach. In this study, we have developed a rigorous method to compare the structure networks of homologous proteins, with a wide range of sequence identity percentages. A range of dissimilarity measures that show the extent of change in the network across homologous structures are generated, which also includes the comparison of the protein structure variability. We discuss in detail, scenarios where the variation of structure is not accompanied by loss or gain of the overall network and its vice versa. The sequence-based phylogeny among the homologs is also compared with the lineage obtained from information from such a robust structure comparison. In summary, we can obtain a quantitative comparison score for the structure networks of homologous proteins, which also enables us to study the evolution of protein function based on the variation of their topologies.

Graph theory approaches for molecular dynamics simulations

Graph theory, a branch of mathematics that focuses on the study of graphs (networks of nodes and edges), provides a robust framework for analysing the structural and functional properties of biomolecules. By leveraging molecular dynamics (MD) simulations, atoms or groups of atoms can be represented as nodes, while their dynamic interactions are depicted as edges. This network-based approach facilitates the characterization of properties such as connectivity, centrality, and modularity, which are essential for understanding the behaviour of molecular systems. This review details the application and development of graph theory-based models in studying biomolecular systems. We introduce key concepts in graph theory and demonstrate their practical applications, illustrating how innovative graph theory approaches can be employed to design biomolecular systems with enhanced functionality. Specifically, we explore the integration of graph theoretical methods with MD simulations to gain deeper insights into complex biological phenomena, such as allosteric regulation, conformational dynamics, and catalytic functions. Ultimately, graph theory has proven to be a powerful tool in the field of molecular dynamics, offering valuable insights into the structural properties, dynamics, and interactions of molecular systems. This review establishes a foundation for using graph theory in molecular design and engineering, highlighting its potential to transform the field and drive advancements in the understanding and manipulation of biomolecular systems.

Osmolyte-induced protein stability changes explained by graph theory

Enhanced stabilization of protein structures via the presence of inert osmolytes is a key mechanism adopted both by physiological systems and in biotechnological applications. While the intrinsic stability of proteins is ultimately fixed by their amino acid composition and organization, the interactions between osmolytes and proteins together with their concentrations introduce an additional layer of complexity and in turn, a method of modulating protein stability. Here, we combined experimental measurements with molecular dynamics simulations and graph-theory-based analyses to predict the stabilizing/destabilizing effects of different kinds of osmolytes on proteins during heat-mediated denaturation. We found that (i) proteins in solution with stability-enhancing osmolytes tend to have more compact interaction networks than those assumed in the presence of destabilizing osmolytes; (ii) a strong negative correlation (R = -0.85) characterizes the relationship between the melting temperatureT m and the preferential interaction coefficient defined by the radial distribution functions of osmolytes and water around the protein and (iii) a positive correlation exists between osmolyte-osmolyte clustering and the extent of preferential exclusion from the local domain of the protein, suggesting that exclusion may be driven by enhanced steric hindrance of aggregated osmolytes.

Structural plasticity of arrestin-G protein coupled receptor complexes as a molecular determinant of signaling

G protein coupled receptors (GPCRs) are critically regulated by arrestins. In this study, high-resolution data was combined with molecular dynamics simulations to infer the determinants of β-arrestin 1 (βarr1)-GPCR coupling, using the V2 vasopressin receptor (V2R) as a model system. The study highlighted the extremely high plasticity of βarr1-GPCR complexes, dependent on receptor type, state, and membrane environment. The multiple functions of receptor-bound βarr1 are likely determined by the interplay of intrinsic flexibility and collective motions both as a bi-domain protein and as a whole. The two major collective motions of the whole βarr1, consisting in rotation parallel to the membrane plane and inclination with respect to the receptor main axis, are distinctly linked to the two intermolecular interfaces involved in tail and core interactions. The intermolecular dynamic coupling between βarr1 and V2R depends on the allosteric effect of the agonist arginine-vasopressin (AVP). In the absence of AVP the dynamic coupling concerns only tail interactions, while in the presence of AVP it involves both tail and core interactions. This suggests that constitutive and agonist-induced arrestin-receptor dynamic coupling is linked to distinct arrestin functions.

Exploring Druggable Binding Sites on the Class A GPCRs Using the Residue Interaction Network and Site Identification by Ligand Competitive Saturation

G protein-coupled receptors (GPCRs) play a central role in cellular signaling and are linked to many diseases. Accordingly, computational methods to explore potential allosteric sites for this class of proteins to facilitate the identification of potential modulators are needed. Importantly, the availability of rich structural data providing the locations of the orthosteric ligands and allosteric modulators targeting different GPCRs allows for the validation of approaches to identify new allosteric binding sites. Here, we validate the combination of two computational techniques, the residue interaction network (RIN) model and the site identification by ligand competitive saturation (SILCS) method, to predict putative allosteric binding sites of class A GPCRs. RIN analysis identifies hub residues that mediate allosteric signaling within a receptor and have a high capacity to alter receptor dynamics upon ligand binding. The known orthosteric (and allosteric) binding sites of 18 distinct class A GPCRs were successfully predicted by RIN through a dataset of 105 crystal structures (91 ligand-bound, 14 unbound) with up to 77.8% (76.9%) sensitivity, 92.5% (95.3%) specificity, 51.9% (50%) precision, and 86.2% (92.4%) accuracy based on the experimental and theoretical binding site data. Moreover, graph spectral analysis of the residue networks revealed that the proposed sites were located at the interfaces of highly interconnected residue clusters with a high ability to coordinate the functional dynamics. Then, we employed the SILCS-Hotspots method to assess the druggability of the novel sites predicted for 7 distinct class A GPCRs that are critical for a variety of diseases. While the known orthosteric and allosteric binding sites are successfully explored by our approach, numerous putative allosteric sites with the potential to bind drug-like molecules are proposed. The computational approach presented here promises to be a highly effective tool to predict putative allosteric sites of GPCRs to facilitate the design of effective modulators.

Dual-site molecular glues for enhancing protein-protein interactions of the CDK12-DDB1 complex

Protein-protein interactions (PPIs) stabilization with molecular glues plays a crucial role in drug discovery, albeit with significant challenges. In this study, we propose a dual-site approach, targeting the PPI region and its dynamic surroundings. We conduct molecular dynamics simulations to identify critical sites on the PPI that stabilize the cyclin-dependent kinase 12 - DNA damage-binding protein 1 (CDK12-DDB1) complex, resulting in further cyclin K degradation. This exploration leads to the creation of LL-K12-18, a dual-site molecular glue, which enhances the glue properties to augment degradation kinetics and efficiency. Notably, LL-K12-18 demonstrates strong inhibition of gene transcription and anti-proliferative effects in tumor cells, showing significant potency improvements in MDA-MB-231 (88-fold) and MDA-MB-468 cells (307-fold) when compared to its precursor compound SR-4835. These findings underscore the potential of dual-site approaches in disrupting CDK12 function and offer a structural insight-based framework for the design of cyclin K molecular glues.

Reviewing the Structure-Function Paradigm in Polyglutamine Disorders: A Synergistic Perspective on Theoretical and Experimental Approaches

Polyglutamine (polyQ) disorders are a group of neurodegenerative diseases characterized by the excessive expansion of CAG (cytosine, adenine, guanine) repeats within host proteins. The quest to unravel the complex diseases mechanism has led researchers to adopt both theoretical and experimental methods, each offering unique insights into the underlying pathogenesis. This review emphasizes the significance of combining multiple approaches in the study of polyQ disorders, focusing on the structure-function correlations and the relevance of polyQ-related protein dynamics in neurodegeneration. By integrating computational/theoretical predictions with experimental observations, one can establish robust structure-function correlations, aiding in the identification of key molecular targets for therapeutic interventions. PolyQ proteins' dynamics, influenced by their length and interactions with other molecular partners, play a pivotal role in the polyQ-related pathogenic cascade. Moreover, conformational dynamics of polyQ proteins can trigger aggregation, leading to toxic assembles that hinder proper cellular homeostasis. Understanding these intricacies offers new avenues for therapeutic strategies by fine-tuning polyQ kinetics, in order to prevent and control disease progression. Last but not least, this review highlights the importance of integrating multidisciplinary efforts to advancing research in this field, bringing us closer to the ultimate goal of finding effective treatments against polyQ disorders.

AbFlex: designing antibody complementarity determining regions with flexible CDR definition

MOTIVATION

Antibodies are proteins that the immune system produces in response to foreign pathogens. Designing antibodies that specifically bind to antigens is a key step in developing antibody therapeutics. The complementarity determining regions (CDRs) of the antibody are mainly responsible for binding to the target antigen, and therefore must be designed to recognize the antigen.

RESULTS

We develop an antibody design model, AbFlex, that exhibits state-of-the-art performance in terms of structure prediction accuracy and amino acid recovery rate. Furthermore, >38% of newly designed antibody models are estimated to have better binding energies for their antigens than wild types. The effectiveness of the model is attributed to two different strategies that are developed to overcome the difficulty associated with the scarcity of antibody-antigen complex structure data. One strategy is to use an equivariant graph neural network model that is more data-efficient. More importantly, a new data augmentation strategy based on the flexible definition of CDRs significantly increases the performance of the CDR prediction model.

AVAILABILITY AND IMPLEMENTATION

The source code and implementation are available at https://github.com/wsjeon92/AbFlex.

Revisiting structural organization of proteins at high temperature from a network perspective

Interactions between distantly placed amino acids in the primary chain (long-range) play a very crucial role in the formation and stabilization of the tertiary structure of a protein, while interactions between closely placed amino acids in the primary chain (short-range) mostly stabilize the secondary structures. Every protein needs to maintain marginal stability in order to perform its physiological functions in its native environment. The requirements for this stability in mesophilic and thermophilic proteins are different. Thermophilic proteins need to form more interactions as well as more stable interactions to survive in the extreme environment, they live in. Here, we aim to find out how the interacting amino acids in three-dimensional space are positioned in the primary chains in thermophilic and mesophilic. How does this arrangement help thermophiles to maintain their structural integrity at high temperatures? Working on a dataset of 1560 orthologous pairs we perceive that thermophiles are not only enriched with long-range interactions, they feature bigger connected clusters and higher network densities compared to their mesophilic orthologs, at higher interaction strengths between the amino acids. Moreover, we have observed the enrichment of different types of interactions at different secondary structural regions.

Biological importance of arginine: A comprehensive review of the roles in structure, disorder, and functionality of peptides and proteins

Arginine shows Jekyll and Hyde behavior in several respects. It participates in protein folding via ionic and H-bonds and cation-pi interactions; the charge and hydrophobicity of its side chain make it a disorder-promoting amino acid. Its methylation in histones; RNA binding proteins; chaperones regulates several cellular processes. The arginine-centric modifications are important in oncogenesis and as biomarkers in several cardiovascular diseases. The cross-links involving arginine in collagen and cornea are involved in pathogenesis of tissues but have also been useful in tissue engineering and wound-dressing materials. Arginine is a part of active site of several enzymes such as GTPases, peroxidases, and sulfotransferases. Its metabolic importance is obvious as it is involved in production of urea, NO, ornithine and citrulline. It can form unusual functional structures such as molecular tweezers in vitro and sprockets which engage DNA chains as part of histones in vivo. It has been used in design of cell-penetrating peptides as drugs. Arginine has been used as an excipient in both solid and injectable drug formulations; its role in suppressing opalescence due to liquid-liquid phase separation is particularly very promising. It has been known as a suppressor of protein aggregation during protein refolding. It has proved its usefulness in protein bioseparation processes like ion-exchange, hydrophobic and affinity chromatographies. Arginine is an amino acid, whose importance in biological sciences and biotechnology continues to grow in diverse ways.

Prioritization of Trypanosoma brucei editosome protein interactions interfaces at residue resolution through proteome-scale network analysis

BACKGROUND

Trypanosoma brucei is the causative agent for trypanosomiasis in humans and livestock, which presents a growing challenge due to drug resistance. While identifying novel drug targets is vital, the process is delayed due to a lack of functional information on many of the pathogen's proteins. Accordingly, this paper presents a computational framework for prioritizing drug targets within the editosome, a vital molecular machinery responsible for mitochondrial RNA processing in T. brucei. Importantly, this framework may eliminate the need for prior gene or protein characterization, potentially accelerating drug discovery efforts.

RESULTS

By integrating protein-protein interaction (PPI) network analysis, PPI structural modeling, and residue interaction network (RIN) analysis, we quantitatively ranked and identified top hub editosome proteins, their key interaction interfaces, and hotspot residues. Our findings were cross-validated and further prioritized by incorporating them into gene set analysis and differential expression analysis of existing quantitative proteomics data across various life stages of T. brucei. In doing so, we highlighted PPIs such as KREL2-KREPA1, RESC2-RESC1, RESC12A-RESC13, and RESC10-RESC6 as top candidates for further investigation. This includes examining their interfaces and hotspot residues, which could guide drug candidate selection and functional studies.

CONCLUSION

RNA editing offers promise for target-based drug discovery, particularly with proteins and interfaces that play central roles in the pathogen's life cycle. This study introduces an integrative drug target identification workflow combining information from the PPI network, PPI 3D structure, and reside-level information of their interface which can be applicable to diverse pathogens. In the case of T. brucei, via this pipeline, the present study suggested potential drug targets with residue-resolution from RNA editing machinery. However, experimental validation is needed to fully realize its potential in advancing urgently needed antiparasitic drug development.

AlphaFold2-Enabled Atomistic Modeling of Epistatic Binding Mechanisms for the SARS-CoV-2 Spike Omicron XBB.1.5, EG.5 and FLip Variants: Convergent Evolution Hotspots Cooperate to Control Stability and Conformational Adaptability in Balancing ACE2 Binding and Antibody Resistance

In this study, we combined AI-based atomistic structural modeling and microsecond molecular simulations of the SARS-CoV-2 Spike complexes with the host receptor ACE2 for XBB.1.5+L455F, XBB.1.5+F456L(EG.5) and XBB.1.5+L455F/F456L (FLip) lineages to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and physics-based rigorous computations of binding affinities, we identified binding energy hotspots and characterized molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of Q493 hotspot in synchronization of epistatic couplings between L455F and F456L mutations providing a quantitative insight into the mechanism underlying differences between XBB lineages. Mutational profiling is combined with network-based model of epistatic couplings showing that the Q493, L455 and F456 sites mediate stable communities at the binding interface with ACE2 and can serve as stable mediators of non-additive couplings. Structure-based mutational analysis of Spike protein binding with the class 1 antibodies quantified the critical role of F456L and F486P mutations in eliciting strong immune evasion response. The results of this analysis support a mechanism in which the emergence of EG.5 and FLip variants may have been dictated by leveraging strong epistatic effects between several convergent revolutionary hotspots that provide synergy between the improved ACE2 binding and broad neutralization resistance. This interpretation is consistent with the notion that functionally balanced substitutions which simultaneously optimize immune evasion and high ACE2 affinity may continue to emerge through lineages with beneficial pair or triplet combinations of RBD mutations involving mediators of epistatic couplings and sites in highly adaptable RBD regions.

Exploring the molecular mechanism of cold-adaption of an alkaline protease mutant by molecular dynamics simulations and residue interaction network

Psychrophilic proteases have attracted enormous attention in past decades, due to their high catalytic activity at low temperatures in a wide range of industrial processes, especially in the detergent and leather industries. Among them, H5 is an alkaline protease mutant, which featuring psychrophilic-like behavior, but the reasons that H5 with higher activity at low temperatures are still poorly understood. Herein, the molecular dynamics (MD) simulations combined with residue interaction network (RIN) were utilized to investigate the mechanisms of the cold-adaption of mutant H5. The results demonstrated that two loops involved in the substrate binding G100-S104 and S125-S129 in H5 had higher mobility, and the distance enlargement between the two loops modulated the substrate's accessibility compared with wild type counterpart. Besides, H5 enhanced conformational flexibility by weakening salt bridges and increasing interaction with the solvent. In particular, the absence of Lys251-Asp197-Arg247 salt bridge network may contribute to the structural mobility. Based on the free energy landscape and molecular mechanics Poisson-Boltzmann surface area of the wild type and H5, it was elucidated that H5 possessed a large population of interconvertible conformations, resulting in the weaker substrate binding free energy. The calculated RIN topology parameters such as the average degree, average cluster coefficient, and average path length further verified that the mutant H5 attenuated residue-to-residue interactions. The investigation of the mechanisms by which how the residue mutation affects the stability and activity of enzymes provides a theoretical basis for the development of cold-adapted protease.

PyPCN: protein contact networks in PyMOL

MOTIVATION

Protein contact networks (PCNs) represent the 3D structure of a protein using network formalism. Inter-residue contacts are described as binary adjacency matrices, which are derived from the graph representation of residues (as α-carbons, β-carbons or centroids) and Euclidean distances according to defined thresholds. Functional characterization algorithms are computed on binary adjacency matrices to unveil allosteric, dynamic, and interaction mechanisms in proteins. Such strategies are usually applied in a combinatorial manner, although rarely in seamless and user-friendly implementations.

RESULTS

PyPCN is a plugin for PyMOL wrapping more than twenty PCN algorithms and metrics in an easy-to-use graphical user interface, to support PCN analysis. The plugin accepts 3D structures from the Protein Data Bank, user-provided PDBs, or precomputed adjacency matrices. The results are directly mapped to 3D protein structures and organized into interactive diagrams for their visualization. A dedicated graphical user interface combined with PyMOL visual support makes analysis more intuitive and easier, extending the applicability of PCNs.

AVAILABILITY AND IMPLEMENTATION

https://github.com/pcnproject/PyPCN.

Deep learning in preclinical antibody drug discovery and development

Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development.

Probing conformational landscapes of binding and allostery in the SARS-CoV-2 omicron variant complexes using microsecond atomistic simulations and perturbation-based profiling approaches: hidden role of omicron mutations as modulators of allosteric signaling and epistatic relationships

In this study, we systematically examine the conformational dynamics, binding and allosteric communications in the Omicron BA.1, BA.2, BA.3 and BA.4/BA.5 spike protein complexes with the ACE2 host receptor using molecular dynamics simulations and perturbation-based network profiling approaches. Microsecond atomistic simulations provided a detailed characterization of the conformational landscapes and revealed the increased thermodynamic stabilization of the BA.2 variant which can be contrasted with the BA.4/BA.5 variants inducing a significant mobility of the complexes. Using the dynamics-based mutational scanning of spike residues, we identified structural stability and binding affinity hotspots in the Omicron complexes. Perturbation response scanning and network-based mutational profiling approaches probed the effect of the Omicron mutations on allosteric interactions and communications in the complexes. The results of this analysis revealed specific roles of Omicron mutations as conformationally plastic and evolutionary adaptable modulators of binding and allostery which are coupled to the major regulatory positions through interaction networks. Through perturbation network scanning of allosteric residue potentials in the Omicron variant complexes performed in the background of the original strain, we characterized regions of epistatic couplings that are centered around the binding affinity hotspots N501Y and Q498R. Our results dissected the vital role of these epistatic centers in regulating protein stability, efficient ACE2 binding and allostery which allows for accumulation of multiple Omicron immune escape mutations at other sites. Through integrative computational approaches, this study provides a systematic analysis of the effects of Omicron mutations on thermodynamics, binding and allosteric signaling in the complexes with ACE2 receptor.

PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles

Due to the complex nature of noncovalent interactions and their long-range effects, analyzing protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) provide a convenient formalism to study protein structures in relation to essential properties such as key residues for structural stability, allosteric communication, and the effects of modifications of the protein. PSNs can be defined according to very different principles, and the available tools have limitations in input formats, supported models, and version control. Other outstanding problems are related to the definition of network cutoffs and the assessment of the stability of the network properties. The protein science community could benefit from a common framework to carry out these analyses and make them easier to reproduce, reuse, and evaluate. We here provide two open-source software packages, PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a reproducible and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and incorporates different network models with the possibility of integrating them into a macronetwork and performing various downstream analyses, including hubs, connected components, and several other centrality measures, and visualizes the networks or further analyzes them thanks to compatibility with Cytoscape.PyInKnife2 that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. We foresee that the modular structure of the code and the supported version control system will promote the transition to a community-driven effort, boost reproducibility, and establish common protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities and maintenance, assistance, and training of new contributors.

Crystal structure of a variable region segment of Leptospira host-interacting outer surface protein, LigA, reveals the orientation of Ig-like domains

Leptospiral immunoglobulin-like (Lig) protein family is a surface-exposed protein from the pathogenic Leptospira. The Lig protein family has been identified as an essential virulence factor of L. interrogan. One of the family members, LigA, contains 13 homologous tandem repeats of bacterial Ig-like (Big) domains in its extracellular portion. It is crucial in binding with the host's Extracellular matrices (ECM) and complement factors. However, its vital role in the invasion and evasion of pathogenic Leptospira, structural details, and domain organization of the extracellular portion of this protein are not explored thoroughly. Here, we described the first high-resolution crystal structure of a variable region segment (LigA8-9) of LigA at 1.87 Å resolution. The structure showed some remarkably distinctive aspects compared with the most closely related Immunoglobulin superfamily (IgSF) members. The structure illustrated the relative orientation of two domains and highlighted the role of the linker region in the domain orientation. We also observed an apparent electron density of Ca²⁺ ions coordinated with a proper interacting geometry within the protein. Molecular dynamic simulations demonstrated the involvement of a linker salt bridge in providing rigidity between the two domains. Our study proposes an overall arrangement of Ig-like domains in the LigA protein. The structural understanding of the extracellular portion of LigA and its interaction with the ECM provides insight into developing new therapeutics directed toward leptospirosis.

Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling a balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility.

Probing Conformational Landscapes of Binding and Allostery in the SARS-CoV-2 Omicron Variant Complexes Using Microsecond Atomistic Simulations and Perturbation-Based Profiling Approaches: Hidden Role of Omicron Mutations as Modulators of Allosteric Signaling and Epistatic Relationships

In this study, we systematically examine the conformational dynamics, binding and allosteric communications in the Omicron BA.1, BA.2, BA.3 and BA.4/BA.5 complexes with the ACE2 host receptor using molecular dynamics simulations and perturbation-based network profiling approaches. Microsecond atomistic simulations provided a detailed characterization of the conformational landscapes and revealed the increased thermodynamic stabilization of the BA.2 variant which is contrasted with the BA.4/BA.5 variants inducing a significant mobility of the complexes. Using ensemble-based mutational scanning of binding interactions, we identified binding affinity and structural stability hotspots in the Omicron complexes. Perturbation response scanning and network-based mutational profiling approaches probed the effect of the Omicron variants on allosteric communications. The results of this analysis revealed specific roles of Omicron mutations as "plastic and evolutionary adaptable" modulators of binding and allostery which are coupled to the major regulatory positions through interaction networks. Through perturbation network scanning of allosteric residue potentials in the Omicron variant complexes, which is performed in the background of the original strain, we identified that the key Omicron binding affinity hotspots N501Y and Q498R could mediate allosteric interactions and epistatic couplings. Our results suggested that the synergistic role of these hotspots in controlling stability, binding and allostery can enable for compensatory balance of fitness tradeoffs with conformationally and evolutionary adaptable immune-escape Omicron mutations. Through integrative computational approaches, this study provides a systematic analysis of the effects of Omicron mutations on thermodynamics, binding and allosteric signaling in the complexes with ACE2 receptor. The findings support a mechanism in which Omicron mutations can evolve to balance thermodynamic stability and conformational adaptability in order to ensure proper tradeoff between stability, binding and immune escape.

Coarse-Grained Molecular Simulations and Ensemble-Based Mutational Profiling of Protein Stability in the Different Functional Forms of the SARS-CoV-2 Spike Trimers: Balancing Stability and Adaptability in BA.1, BA.2 and BA.2.75 Variants

Evolutionary and functional studies have suggested that the emergence of Omicron variants can be determined by multiple fitness tradeoffs including immune escape, binding affinity, conformational plasticity, protein stability, and allosteric modulation. In this study, we embarked on a systematic comparative analysis of the conformational dynamics, electrostatics, protein stability, and allostery in the different functional states of spike trimers for BA.1, BA.2, and BA.2.75 variants. Using efficient and accurate coarse-grained simulations and atomistic reconstruction of the ensembles, we examined the conformational dynamics of the spike trimers that agree with the recent functional studies, suggesting that BA.2.75 trimers are the most stable among these variants. A systematic mutational scanning of the inter-protomer interfaces in the spike trimers revealed a group of conserved structural stability hotspots that play a key role in the modulation of functional dynamics and are also involved in the inter-protomer couplings through local contacts and interaction networks with the Omicron mutational sites. The results of mutational scanning provided evidence that BA.2.75 trimers are more stable than BA.2 and comparable in stability to the BA.1 variant. Using dynamic network modeling of the S Omicron BA.1, BA.2, and BA.2.75 trimers, we showed that the key network mediators of allosteric interactions are associated with the major stability hotspots that are interconnected along potential communication pathways. The network analysis of the BA.1, BA.2, and BA.2.75 trimers suggested that the increased thermodynamic stability of the BA.2.75 variant may be linked with the organization and modularity of the residue interaction network that allows for allosteric communications between structural stability hotspots and Omicron mutational sites. This study provided a plausible rationale for a mechanism in which Omicron mutations may evolve by targeting vulnerable sites of conformational adaptability to elicit immune escape while maintaining their control on balancing protein stability and functional fitness through robust allosteric communications with the stability hotspots.

The quaternary question: Determining allostery in spastin through dynamics classification learning and bioinformatics

The nanomachine from the ATPases associated with various cellular activities superfamily, called spastin, severs microtubules during cellular processes. To characterize the functionally important allostery in spastin, we employed methods from evolutionary information, to graph-based networks, to machine learning applied to atomistic molecular dynamics simulations of spastin in its monomeric and the functional hexameric forms, in the presence or absence of ligands. Feature selection, using machine learning approaches, for transitions between spastin states recognizes all the regions that have been proposed as allosteric or functional in the literature. The analysis of the composition of the Markov State Model macrostates in the spastin monomer, and the analysis of the direction of change in the top machine learning features for the transitions, indicate that the monomer favors the binding of ATP, which primes the regions involved in the formation of the inter-protomer interfaces for binding to other protomer(s). Allosteric path analysis of graph networks, built based on the cross-correlations between residues in simulations, shows that perturbations to a hub specific for the pre-hydrolysis hexamer propagate throughout the structure by passing through two obligatory regions: the ATP binding pocket, and pore loop 3, which connects the substrate binding site to the ATP binding site. Our findings support a model where the changes in the terminal protomers due to the binding of ligands play an active role in the force generation in spastin. The secondary structures in spastin, which are found to be highly degenerative within the network paths, are also critical for feature transitions of the classification models, which can guide the design of allosteric effectors to enhance or block allosteric signaling.

Heterogeneity in winged helix-turn-helix and substrate DNA interactions: Insights from theory and experiments

Specific interactions between transcription factors (TFs) and substrate DNA constitute the fundamental basis of gene expression. Unlike in TFs like basic helix-loop-helix or basic leucine zippers, prediction of substrate DNA is extremely challenging for helix-turn-helix (HTH). Experimental techniques like chromatin immunoprecipitation combined with massively parallel DNA sequencing remains a viable option. We characterize the molecular basis of heterogeneity in HTH-DNA interaction using in silico tools and thence validate them experimentally. Given the profound functional diversity in HTH, we focus primarily on winged-HTH (wHTH). We consider 180 wHTH TFs, whose experimental three-dimensional structures are available in DNA bound/unbound conformations. Starting with PDB-wide scanning and curation of data, we construct a phylogenetic tree, which distributes 180 wHTH sequences under multiple sub-groups. Structure-sequence alignment followed by detailed intra/intergroup analysis, covariation studies and extensive network theory analysis help us to gain deep insight into heterogeneous wHTH-substrate DNA interactions. A central aim of this study is to find a consensus to predict the substrate DNA sequence for wHTH, amidst heterogeneity. The strength of our exhaustive theoretical investigations including molecular docking are successfully tested through experimental characterization of wHTH TF from Sulfurimonas denitrificans.

Evaluating the active site-substrate interplay between x-ray crystal structure and molecular dynamics in chorismate mutase

Designing realistic quantum mechanical (QM) models of enzymes is dependent on reliably discerning and modeling residues, solvents, and cofactors important in crafting the active site microenvironment. Interatomic van der Waals contacts have previously demonstrated usefulness toward designing QM-models, but their measured values (and subsequent residue importance rankings) are expected to be influenceable by subtle changes in protein structure. Using chorismate mutase as a case study, this work examines the differences in ligand-residue interatomic contacts between an x-ray crystal structure and structures from a molecular dynamics simulation. Select structures are further analyzed using symmetry adapted perturbation theory to compute ab initio ligand-residue interaction energies. The findings of this study show that ligand-residue interatomic contacts measured for an x-ray crystal structure are not predictive of active site contacts from a sampling of molecular dynamics frames. In addition, the variability in interatomic contacts among structures is not correlated with variability in interaction energies. However, the results spotlight using interaction energies to characterize and rank residue importance in future computational enzymology workflows.

Wordom update 2: A user-friendly program for the analysis of molecular structures and conformational ensembles

We present the second update of Wordom, a user-friendly and efficient program for manipulation and analysis of conformational ensembles from molecular simulations. The actual update expands some of the existing modules and adds 21 new modules to the update 1 published in 2011. The new adds can be divided into three sets that: 1) analyze atomic fluctuations and structural communication; 2) explore ion-channel conformational dynamics and ionic translocation; and 3) compute geometrical indices of structural deformation. Set 1 serves to compute correlations of motions, find geometrically stable domains, identify a dynamically invariant core, find changes in domain-domain separation and mutual orientation, perform wavelet analysis of large-scale simulations, process the output of principal component analysis of atomic fluctuations, perform functional mode analysis, infer regions of mechanical rigidity, analyze overall fluctuations, and perform the perturbation response scanning. Set 2 includes modules specific for ion channels, which serve to monitor the pore radius as well as water or ion fluxes, and measure functional collective motions like receptor twisting or tilting angles. Finally, set 3 includes tools to monitor structural deformations by computing angles, perimeter, area, volume, β-sheet curvature, radial distribution function, and center of mass. The ring perception module is also included, helpful to monitor supramolecular self-assemblies. This update places Wordom among the most suitable, complete, user-friendly, and efficient software for the analysis of biomolecular simulations. The source code of Wordom and the relative documentation are available under the GNU general public license at http://wordom.sf.net.

Network Hamiltonian Models for Unstructured Protein Aggregates, with Application to γD-Crystallin

Network Hamiltonian models (NHMs) are a framework for topological coarse-graining of protein-protein interactions, in which each node corresponds to a protein, and edges are drawn between nodes representing proteins that are noncovalently bound. Here, this framework is applied to aggregates of γD-crystallin, a structural protein of the eye lens implicated in cataract disease. The NHMs in this study are generated from atomistic simulations of equilibrium distributions of wild-type and the cataract-causing variant W42R in solution, performed by Wong, E. K.; Prytkova, V.; Freites, J. A.; Butts, C. T.; Tobias, D. J. Molecular Mechanism of Aggregation of the Cataract-Related γD-Crystallin W42R Variant from Multiscale Atomistic Simulations. Biochemistry2019, 58 (35), 3691-3699. Network models are shown to successfully reproduce the aggregate size and structure observed in the atomistic simulation, and provide information about the transient protein-protein interactions therein. The system size is scaled from the original 375 monomers to a system of 10000 monomers, revealing a lowering of the upper tail of the aggregate size distribution of the W42R variant. Extrapolation to higher and lower concentrations is also performed. These results provide an example of the utility of NHMs for coarse-grained simulation of protein systems, as well as their ability to scale to large system sizes and high concentrations, reducing computational costs while retaining topological information about the system.

Allosteric Signaling in PDZ Energetic Networks: Embedding Error Analysis

Allosteric signaling in proteins has been known for some half a century, yet how the signal traverses the protein remains an active area of research. Recently, the importance of electrostatics to achieve long-range signaling has become increasingly appreciated. Our laboratory has been working on developing network approaches to capture such interactions. In this study, we turn our attention to the well-studied allosteric model protein, PDZ. We study the allosteric dynamics on a per-residue basis in key constructs involving the PDZ domain, its allosteric effector, and its peptide ligand. We utilize molecular dynamics trajectories to create the networks for the constructs to explore the allosteric effect by plotting the heat kernel results onto axes defined by principal components. We introduce a new metric to quantitate the volume sampled by a residue in the latent space. We relate our findings to PDZ and the greater field of allostery.

Deciphering the Structural Determinants Critical in Attaining the FXR Partial Agonism

Elucidation of structural determinants is pivotal for structure-based drug discovery. The Farnesoid X receptor (FXR) is a proven target for NASH; however, its full agonism causes certain clinical complications. Therefore, partial agonism (PA) appears as a viable alternative for improved therapeutics. Since the agonist and PA both share the same binding site, i.e., ligand-binding pocket (LBP), which is highly dynamic and has synergy with the substrate binding site, the selective designing of PA is challenging. The identification of structural and conformational determinants is critical for PA compared with an agonist. Furthermore, the mechanism by which PA modulates the structural dynamics of FXR at the residue level, a prerequisite for PA designing, is still elusive. Here, by using ∼4.5 μs of MD simulations and residue-wise communication network analysis, we identified the structural regions which are flexible with PA but frozen with an agonist. Also, the network analysis identified the considerable changes between an agonist and PA in biologically essential zones of FXR such as helix H10/H11 and loop L:H11/H12, which lead to the modulation of synergy between LBP and the substrate binding site. Furthermore, the thermodynamic profiling suggested the methionine residues, mainly M³²⁸, M³⁶⁵, and M⁴⁵⁰, seem to be responsible for the recruitment of PA. The other residues I³⁵⁷, Y³⁶¹, L⁴⁶⁵, F³⁰⁸, Q³¹⁶, and K³²¹ are also identified, exclusively interacting with PA. This study offers novel structural and mechanistic insights that are critical for FXR targeted drug discovery for PA designing.

psnGPCRdb: The Structure-network Database of G Protein Coupled Receptors

G protein coupled receptors (GPCRs) are critical eukaryotic signal transduction gatekeepers and represent the largest protein superfamily in the human proteome, with more than 800 members. They share seven transmembrane helices organized in an up-down bundle architecture. GPCR-mediated signaling pathways have been linked to numerous human diseases, and GPCRs are the targets of approximately 35% of all drugs currently on the market. Structure network analysis, a graph theory-based approach, represents a cutting-edge tool to deeply understand GPCR function, which strongly relies on communication between the extracellular and intracellular poles of their structure. psnGPCRdb stores the structure networks (i.e., linked nodes, hubs, communities and communication pathways) computed on all updated GPCR structures in the Protein Data Bank, in their isolated states or in complex with extracellular and/or intracellular molecules. The structure communication signatures of a sub-family or family of GPCRs as well as of their small-molecule activators or inhibitors are stored as consensus networks. The database stores also all meaningful structure network-based comparisons (i.e., difference networks) of functionally different states (i.e., inactive or active) of a given receptor sub-type, or of consensus networks representative of a receptor sub-type, type, sub-family or family. Single or consensus GPCR networks hold also information on amino acid conservation. The database allows to graphically analyze 3D structure networks together with interactive data-tables. Ligand-centric networks can be analyzed as well. psnGPCRdb is unique and represents a powerful resource to unravel GPCR function with important implications in cell signaling and drug design. psnGPCRdb is freely available at: http://webpsn.hpc.unimo.it/psngpcr.php.

Structural and functional analysis of a tandem repeat galacturonic acid-binding lectin from the sea hare Aplysia californica

A d-galacturonic acid-specific lectin, named AcL, was purified from the sea hare Aplysia californica by galactose-agarose affinity chromatography. AcL has a molecular mass of 27.5 kDa determined by MALDI-TOF mass spectrometry. This lectin shows a good affinity for d-galacturonic acid and a lower affinity for galactosides: raffinose, melibiose, α and β-lactose, and d-galactose. We determined the amino acid sequence of AcL by trypsin digestion and subsequent peptide analysis by mass spectrometry, resulting in a 238 amino acid protein with a theoretical molecular mass of 26.4 kDa. The difference between the theoretical and experimental values can be attributed to post-translational modifications. Thiol-disulfide quantification discerned five disulfide bonds and three free cysteines. The structure of Acl is mainly comprised of beta sheets, determined by circular dichroism, and predicted with AlphaFold. Theoretical models depict three nearly identical tandem domains consisting of two beta sheets each. From docking analysis, we identified AcL glycan-binding sites as multiple conserved motifs in each domain. Furthermore, phylogenetic analysis based on its structure and sequence showed that AcL and its closest homologues (GalULs) form a clear monophyletic group, distinct from other glycan-binding proteins with a jelly-roll fold: lectins of types F and H. GalULs possess four conserved sequence regions that distinguish them and are either ligand-binding motifs or stabilizing network hubs. We suggest that this new family should be referred to as GalUL or D-type, following the traditional naming of lectins; D standing for depilans, the epithet for the species (Aplysia depilans) from which a lectin of this family was first isolated and described.

Probing conformational landscapes and mechanisms of allosteric communication in the functional states of the ABL kinase domain using multiscale simulations and network-based mutational profiling of allosteric residue potentials

In the current study, multiscale simulation approaches and dynamic network methods are employed to examine the dynamic and energetic details of conformational landscapes and allosteric interactions in the ABL kinase domain that determine the kinase functions. Using a plethora of synergistic computational approaches, we elucidate how conformational transitions between the active and inactive ABL states can employ allosteric regulatory switches to modulate intramolecular communication networks between the ATP site, the substrate binding region, and the allosteric binding pocket. A perturbation-based network approach that implements mutational profiling of allosteric residue propensities and communications in the ABL states is proposed. Consistent with biophysical experiments, the results reveal functionally significant shifts of the allosteric interaction networks in which preferential communication paths between the ATP binding site and substrate regions in the active ABL state become suppressed in the closed inactive ABL form, which in turn features favorable allosteric coupling between the ATP site and the allosteric binding pocket. By integrating the results of atomistic simulations with dimensionality reduction methods and Markov state models, we analyze the mechanistic role of macrostates and characterize kinetic transitions between the ABL conformational states. Using network-based mutational scanning of allosteric residue propensities, this study provides a comprehensive computational analysis of long-range communications in the ABL kinase domain and identifies conserved regulatory hotspots that modulate kinase activity and allosteric crosstalk between the allosteric pocket, ATP binding site, and substrate binding regions.

TMPRSS6 rs855791 polymorphism is associated with iron deficiency in a cohort of Sri Lankan pregnant women

Background

Hepcidin is the key regulator of systemic iron homeostasis and is downregulated by matriptase 2 (MT2), a protease encoded by TMPRSS6 gene. In the presence of low iron levels, MT2 cleaves membrane-bound hemojuvelin (HJV), causing a negative regulation of hepcidin at the gene level, and restores iron balance. rs855791T > C, a missense variant in the catalytic domain of MT2, causes valine to alanine change at 736 position. The current study aimed to investigate the association of TMPRSS6 rs855791 on iron status among a cohort of pregnant women in Sri Lanka and to predict the possible molecular mechanisms.

Methods

The study was conducted among 73 pregnant women at ≤ 12 weeks of gestation. Iron deficiency was defined as serum ferritin < 30 μg/L after adjusting for inflammation. rs855791 was genotyped with a PCR–RFLP, and its association with iron deficiency was analyzed using binary logistic regression. Docking of HJV with MT2 protein encoded by the two rs855791 alleles was undertaken in silico to predict the molecular mechanism of the observed associations.

Results

The majority of the study population (70%) were iron deficient. Among the subjects, T allele was prevalent in the iron deficient group with a frequency of 61.8%, with a nearly twofold enhanced risk for iron deficiency (OR = 2.566, 95%CI; P = 0.011). For TT genotype, the risk of iron deficiency was nearly sixfold (OR = 5.867; 95%CI; P = 0.023). According to the in silico analysis, MT2 736A and HJV complex is more stable with an interface energy of − 7.934 kJ/mol compared to the MT2 736 V and HJV complex which generates an interface energy of − 4.689 kJ/mol.

Conclusion

The current study suggests that the iron regulatory effect of rs855791 of TMPRSS6 is brought about by the differences in thermodynamic stability of the two protein complexes made by MT2 and HJV proteins. The prevalence of iron deficiency observed among Sri Lankan pregnant women may be an interplay between the prevalence of rs855791 T allele and the low dietary iron intake.

Functional Loop Dynamics and Characterization of the Inactive State of the NS2B-NS3 Dengue Protease due to Allosteric Inhibitor Binding

Dengue virus, a flavivirus that causes dengue shock syndrome and dengue hemorrhagic fever, is currently prevalent worldwide. A two-component protease (NS2B-NS3) is essential for maturation, representing an important target for designing anti-flavivirus drugs. Previously, consideration has been centered on developing active-site inhibitors of NS2B-NS3pro. However, the flat and charged nature of its active site renders difficulties in developing inhibitors, suggesting an alternative strategy for identifying allosteric inhibitors. The allosterically sensitive site of the dengue protease is located near Ala125, between the 120s loop and 150s loop. Using atomistic molecular dynamics simulations, we have explored the protease's conformational dynamics upon binding of an allosteric inhibitor. Furthermore, characterization of the inherent flexible loops (71-75s loop, 120s loop, and 150s loop) is carried out for allosteric-inhibitor-bound wild-type and mutant A125C variants and a comparison is performed with its unbound state to extract the structural changes describing the inactive state of the protease. Our study reveals that compared to the unliganded system, the inhibitor-bound system shows large structural changes in the 120s loop and 150s loop in contrast to the rigid 71-75s loop. The unliganded system shows a closed-state pocket in contrast to the open state for the wild-type complex that locks the protease into the open and inactive-state conformations. However, the mutant complex fluctuates between open and closed states. Also, we tried to see how mutation and binding of an allosteric inhibitor perturb the connectivity in a protein structure network (PSN) at contact levels. Altogether, our study reveals the mechanism of conformational rearrangements of loops at the molecular level, locking the protein in an inactive conformation, which may be useful for developing allosteric inhibitors.

Frustration-driven allosteric regulation and signal transmission in the SARS-CoV-2 spike omicron trimer structures: a crosstalk of the omicron mutation sites allosterically regulates tradeoffs of protein stability and conformational adaptability

Dissecting the regulatory principles underlying function and activity of the SARS-CoV-2 spike protein at the atomic level is of paramount importance for understanding the mechanisms of virus transmissibility and immune escape. In this work, we introduce a hierarchical computational approach for atomistic modeling of allosteric mechanisms in the SARS-CoV-2 Omicron spike proteins and present evidence of a frustration-based allostery as an important energetic driver of the conformational changes and spike activation. By examining conformational landscapes and the residue interaction networks in the SARS-CoV-2 Omicron spike protein structures, we have shown that the Omicron mutational sites are dynamically coupled and form a central engine of the allosterically regulated spike machinery that regulates the balance and tradeoffs between conformational plasticity, protein stability, and functional adaptability. We have found that the Omicron mutational sites at the inter-protomer regions form regulatory hotspot clusters that control functional transitions between the closed and open states. Through perturbation-based modeling of allosteric interaction networks and diffusion analysis of communications in the closed and open spike states, we have quantified the allosterically regulated activation mechanism and uncover specific regulatory roles of the Omicron mutations. Atomistic reconstruction of allosteric communication pathways and kinetic modeling using Markov transient analysis reveal that the Omicron mutations form the inter-protomer electrostatic bridges that operate as a network of coupled regulatory switches that could control global conformational changes and signal transmission in the spike protein. The results of this study have revealed distinct and yet complementary roles of the Omicron mutation sites as a network of hotspots that enable allosteric modulation of structural stability and conformational changes which are central for spike activation and virus transmissibility.

Conformational Dynamics and Mechanisms of Client Protein Integration into the Hsp90 Chaperone Controlled by Allosteric Interactions of Regulatory Switches: Perturbation-Based Network Approach for Mutational Profiling of the Hsp90 Binding and Allostery

Understanding the allosteric mechanisms of the Hsp90 chaperone interactions with cochaperones and client protein clientele is fundamental to dissect activation and regulation of many proteins. In this work, atomistic simulations are combined with perturbation-based approaches and dynamic network modeling for a comparative mutational profiling of the Hsp90 binding and allosteric interaction networks in the three Hsp90 maturation complexes with FKBP51 and P23 cochaperones and the glucocorticoid receptor (GR) client. The conformational dynamics signatures of the Hsp90 complexes and dynamics fluctuation analysis revealed how the intrinsic plasticity of the Hsp90 dimer can be modulated by cochaperones and client proteins to stabilize the closed dimer state required at the maturation stage of the ATPase cycle. In silico deep mutational scanning of the protein residues characterized the hot spots of protein stability and binding affinity in the Hsp90 complexes, showing that binding hot spots may often coincide with the regulatory centers that modulate dynamic allostery in the Hsp90 dimer. We introduce a perturbation-based network approach for mutational scanning of allosteric residue potentials and characterize allosteric switch clusters that control mechanism of cochaperone-dependent client recognition and remodeling by the Hsp90 chaperone. The results revealed a conserved network of allosteric switches in the Hsp90 complexes that allow cochaperones and GR protein to become integrated into the Hsp90 system by anchoring to the conformational switch points in the functional Hsp90 regions. This study suggests that the Hsp90 binding and allostery may operate under a regulatory mechanism in which activation or repression of the Hsp90 activity can be pre-encoded in the allosterically regulated Hsp90 dimer motions. By binding directly to the conformational switch centers on the Hsp90, cochaperones and interacting proteins can efficiently modulate the allosteric interactions and long-range communications required for client remodeling and activation.

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery

Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody-antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.

Integrating Conformational Dynamics and Perturbation-Based Network Modeling for Mutational Profiling of Binding and Allostery in the SARS-CoV-2 Spike Variant Complexes with Antibodies: Balancing Local and Global Determinants of Mutational Escape Mechanisms

In this study, we combined all-atom MD simulations, the ensemble-based mutational scanning of protein stability and binding, and perturbation-based network profiling of allosteric interactions in the SARS-CoV-2 spike complexes with a panel of cross-reactive and ultra-potent single antibodies (B1-182.1 and A23-58.1) as well as antibody combinations (A19-61.1/B1-182.1 and A19-46.1/B1-182.1). Using this approach, we quantify the local and global effects of mutations in the complexes, identify protein stability centers, characterize binding energy hotspots, and predict the allosteric control points of long-range interactions and communications. Conformational dynamics and distance fluctuation analysis revealed the antibody-specific signatures of protein stability and flexibility of the spike complexes that can affect the pattern of mutational escape. A network-based perturbation approach for mutational profiling of allosteric residue potentials revealed how antibody binding can modulate allosteric interactions and identified allosteric control points that can form vulnerable sites for mutational escape. The results show that the protein stability and binding energetics of the SARS-CoV-2 spike complexes with the panel of ultrapotent antibodies are tolerant to the effect of Omicron mutations, which may be related to their neutralization efficiency. By employing an integrated analysis of conformational dynamics, binding energetics, and allosteric interactions, we found that the antibodies that neutralize the Omicron spike variant mediate the dominant binding energy hotpots in the conserved stability centers and allosteric control points in which mutations may be restricted by the requirements of the protein folding stability and binding to the host receptor. This study suggested a mechanism in which the patterns of escape mutants for the ultrapotent antibodies may not be solely determined by the binding interaction changes but are associated with the balance and tradeoffs of multiple local and global factors, including protein stability, binding affinity, and long-range interactions.

Probing structural basis for enhanced binding of SARS-CoV-2 P.1 variant spike protein with the human ACE2 receptor

The initial step of infection by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) involves the binding of receptor binding domain (RBD) of the spike protein to the angiotensin converting enzyme 2 (ACE2) receptor. Each successive wave of SARS‐CoV‐2 reports emergence of many new variants, which is associated with mutations in the RBD as well as other parts of the spike protein. These mutations are reported to have enhanced affinity towards the ACE2 receptor as well as are also crucial for the virus transmission. Many computational and experimental studies have demonstrated the effect of individual mutation on the RBD‐ACE2 binding. However, the cumulative effect of mutations on the RBD and away from the RBD was not investigated in detail. We report here a comparative analysis on the structural communication and dynamics of the RBD and truncated S1 domain of spike protein in complex with the ACE2 receptor from SARS‐CoV‐2 wild type and its P.1 variant. Our integrative network and dynamics approaches highlighted a subtle conformational changes in the RBD as well as truncated S1 domain of spike protein at the protein contact level, responsible for the increased affinity with the ACE2 receptor. Moreover, our study also identified the commonalities and differences in the dynamics of the interactions between spike protein of SARS‐CoV‐2 wild type and its P.1 variant with the ACE2 receptor. Further, our investigation yielded an understanding towards identification of the unique RBD residues crucial for the interaction with the ACE2 host receptor. Overall, the study provides an insight for designing better therapeutics against the circulating P.1 variants as well as other future variants.

Protein-protein interactions in fatty acid elongase complexes are important for very-long-chain fatty acid synthesis

Fatty acid elongase (FAE), which catalyzes the synthesis of very-long-chain fatty acids (VLCFAs), is a multiprotein complex; however, little is known about its quaternary structure. In this study, bimolecular fluorescence complementation and/or yeast two-hybrid assays showed that homo-interactions were observed in β-ketoacyl-CoA synthases (KCS2, KCS9, and KCS6), Eceriferum2-like proteins [CER2 and CER2-Like2 (C2L2)], and FAE complex proteins (KCR1, PAS2, ECR, and PAS1), except for CER2-Like1 (C2L1). Hetero-interactions were observed between KCSs (KCS2, KCS9, and KCS6), between CER2-LIKEs (CER2, C2L2, and C2L1), and between FAE complex proteins (KCR1, PAS2, ECR, and PAS1). PAS1 interacts with FAE complex proteins (KCR1, PAS2, and ECR), but not with KCSs (KCS2, KCS9, and KCS6) and CER2-LIKEs (CER2, C2L2, and C2L1). Asp308 and Arg309-Arg311 of KCS9 were essential for the homo-interactions of KCS9 and hetero-interactions between KCS9 and PAS2 or ECR. Asp339 of KCS9 is involved in its homo- and hetero-interactions with ECR. Complementation analysis of the Arabidopsis kcs9 mutant by the expression of amino acid-substituted KCS9 mutant genes showed that Asp308 and Asp339 of KCS9 are involved in the synthesis of C24 VLCFAs from C22. This study suggests that protein-protein interaction in FAE complexes is important for VLCFA synthesis and provides insight into the quaternary structure of FAE complexes for efficient synthesis of VLCFAs.

Computer Simulations and Network-Based Profiling of Binding and Allosteric Interactions of SARS-CoV-2 Spike Variant Complexes and the Host Receptor: Dissecting the Mechanistic Effects of the Delta and Omicron Mutations

In this study, we combine all-atom MD simulations and comprehensive mutational scanning of S-RBD complexes with the angiotensin-converting enzyme 2 (ACE2) host receptor in the native form as well as the S-RBD Delta and Omicron variants to (a) examine the differences in the dynamic signatures of the S-RBD complexes and (b) identify the critical binding hotspots and sensitivity of the mutational positions. We also examined the differences in allosteric interactions and communications in the S-RBD complexes for the Delta and Omicron variants. Through the perturbation-based scanning of the allosteric propensities of the SARS-CoV-2 S-RBD residues and dynamics-based network centrality and community analyses, we characterize the global mediating centers in the complexes and the nature of local stabilizing communities. We show that a constellation of mutational sites (G496S, Q498R, N501Y and Y505H) correspond to key binding energy hotspots and also contribute decisively to the key interfacial communities that mediate allosteric communications between S-RBD and ACE2. These Omicron mutations are responsible for both favorable local binding interactions and long-range allosteric interactions, providing key functional centers that mediate the high transmissibility of the virus. At the same time, our results show that other mutational sites could provide a "flexible shield" surrounding the stable community network, thereby allowing the Omicron virus to modulate immune evasion at different epitopes, while protecting the integrity of binding and allosteric interactions in the RBD-ACE2 complexes. This study suggests that the SARS-CoV-2 S protein may exploit the plasticity of the RBD to generate escape mutants, while engaging a small group of functional hotspots to mediate efficient local binding interactions and long-range allosteric communications with ACE2.

Landscape-Based Protein Stability Analysis and Network Modeling of Multiple Conformational States of the SARS-CoV-2 Spike D614G Mutant: Conformational Plasticity and Frustration-Induced Allostery as Energetic Drivers of Highly Transmissible Spike Variants

The structural and functional studies of the SARS-CoV-2 spike protein variants revealed an important role of the D614G mutation that is shared across many variants of concern (VOCs), suggesting the effect of this mutation on the enhanced virus infectivity and transmissibility. The recent structural and biophysical studies provided important evidence about multiple conformational substates of the D614G spike protein. The development of a plausible mechanistic model that can explain the experimental observations from a more unified thermodynamic perspective is an important objective of the current work. In this study, we employed efficient and accurate coarse-grained simulations of multiple structural substates of the D614G spike trimers together with the ensemble-based mutational frustration analysis to characterize the dynamics signatures of the conformational landscapes. By combining the local frustration profiling of the conformational states with residue-based mutational scanning of protein stability and network analysis of allosteric interactions and communications, we determine the patterns of mutational sensitivity in the functional regions and sites of variants. We found that the D614G mutation may induce a considerable conformational adaptability of the open states in the SARS-CoV-2 spike protein without compromising the folding stability and integrity of the spike protein. The results suggest that the D614G mutant may employ a hinge-shift mechanism in which the dynamic couplings between the site of mutation and the interprotomer hinge modulate the interdomain interactions, global mobility change, and the increased stability of the open form. This study proposes that mutation-induced modulation of the conformational flexibility and energetic frustration at the interprotomer interfaces may serve as an efficient mechanism for allosteric regulation of the SARS-CoV-2 spike proteins.

Understanding structural variability in proteins using protein structural networks

Proteins perform their function by accessing a suitable conformer from the ensemble of available conformations. The conformational diversity of a chosen protein structure can be obtained by experimental methods under different conditions. A key issue is the accurate comparison of different conformations. A gold standard used for such a comparison is the root mean square deviation (RMSD) between the two structures. While extensive refinements of RMSD evaluation at the backbone level are available, a comprehensive framework including the side chain interaction is not well understood. Here we employ protein structure network (PSN) formalism, with the non-covalent interactions of side chain, explicitly treated. The PSNs thus constructed are compared through graph spectral method, which provides a comparison at the local and at the global structural level. In this work, PSNs of multiple crystal conformers of single-chain, single-domain proteins, are subject to pair-wise analysis to examine the dissimilarity in their network topologies and in order to determine the conformational diversity of their native structures. This information is utilized to classify the structural domains of proteins into different categories. It is observed that proteins typically tend to retain structure and interactions at the backbone level. However, some of them also depict variability in either their overall structure or only in their inter-residue connectivity at the sidechain level, or both. Variability of sub-networks based on solvent accessibility and secondary structure is studied. The types of specific interactions are found to contribute differently to structure variability. An ensemble analysis by computing the mathematical variance of edge-weights across multiple conformers provided information on the contribution to overall variability from each edge of the PSN. Interactions that are highly variable are identified and their impact on structure variability has been discussed with the help of a case study. The classification based on the present side-chain network-based studies provides a framework to correlate the structure-function relationships in protein structures.

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Monomeric, single domain protein structures can exhibit non-rigid behaviour and be highly variable.

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The comparison of protein structural networks can better discriminate conformations with similar backbones.

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Specific interactions between solvent accessible and inaccessible residues are poorly preserved.

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Network edge-variation offers insights on which interacting residues are likely to influence their dynamics and function.

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These side-chain network-based studies provide a framework to correlate protein structure-function relationships.

Dissecting mutational allosteric effects in alkaline phosphatases associated with different Hypophosphatasia phenotypes: An integrative computational investigation

Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of the ALPL mutations on protein dynamics and function. Statistical analysis of molecular features computed for the ALPL mutations showed a significant difference between the control, mild and severe HPP phenotypes. Molecular dynamics simulations coupled with protein structure network analysis were employed to analyze the effect of single-residue variation on conformational dynamics of TNSALP dimers, and the developed machine learning model suggested that the topological network parameters could serve as a robust indicator of severe mutations. The results indicated that the severity of disease-associated mutations is often linked with mutation-induced modulation of allosteric communications in the protein. This study suggested that ALPL mutations associated with mild and more severe HPPs can exert markedly distinct effects on the protein stability and long-range network communications. By linking the disease phenotypes with dynamic and allosteric molecular signatures, the proposed integrative computational approach enabled to characterize and quantify the allosteric effects of ALPL mutations and role of allostery in the pathogenesis of HPPs.