Data-driven computational analysis of allosteric proteins by exploring protein dynamics, residue coevolution and residue interaction networks

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.

Machine learning and protein allostery

The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.

Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs

Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.

Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins

Protein–protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application between coevolving proteins remains largely uncharacterized. Therefore, we developed the Protein Interactions Calculator (PIC) to efficiently identify coevolving residues between two protein sequences using mutual information. We verified the algorithm using 2102 known human protein interactions and 233 known bacterial protein interactions, with a respective 1975 and 252 non-interacting protein controls. The average PIC score for known human protein interactions was 4.5 times higher than non-interacting proteins (p = 1.03 × 10⁻¹⁰⁸) and 1.94 times higher in bacteria (p = 1.22 × 10⁻³⁵). We then used the PIC scores to determine the probability that two proteins interact. Using those probabilities, we paired 37 Alzheimer’s disease-associated proteins with 8608 other proteins and determined the likelihood that each pair interacts, which we report through a web interface. The PIC had significantly higher sensitivity and residue-specific resolution not available in other algorithms. Therefore, we propose that the PIC can be used to prioritize potential protein interactions, which can lead to a better understanding of biological processes and additional therapeutic targets belonging to protein interaction groups.

Altered Dynamics of S. aureus Phosphofructokinase via Bond Restraints at Two Distinct Allosteric Binding Sites

The effect of perturbation at the allosteric site was investigated through several replicas of molecular dynamics (MD) simulations conducted on bacterial phosphofructokinase (SaPFK). In our previous work, an alternative binding site was estimated to be allosteric in addition to the experimentally reported one. To highlight the effect of both allosteric sites on receptor's dynamics, MD runs were carried out on apo forms with and without perturbation. Perturbation was achieved via incorporating multiple bond restraints for residue pairs located at the allosteric site. Restraints applied to the predicted site caused one dimer to stiffen, whereas an increase in mobility was detected in the same dimer when the experimentally resolved site was restrained. Fluctuations in C_α-C_α distances which is used to disclose residues with high potential of communication indicated a marked increase in signal transmission within each dimer as the receptor switched to a restrained state. Cross-correlation of positional fluctuations indicated an overall decrease in the magnitude of both positive and negative correlations when restraints were employed on the predicted allosteric site whereas an exact opposite effect was observed for the reported site. Finally, mutual correspondence between positional fluctuations noticeably increased with restraints on predicted allosteric site, whereas an opposite effect was observed for restraints applied on experimentally reported one. In view of these findings, it is clear that the perturbation of either one of two allosteric sites effected the dynamics of the receptor with a distinct and contrasting character.

Prediction of DNA-Binding Protein–Drug-Binding Sites Using Residue Interaction Networks and Sequence Feature

Identification of protein–ligand binding sites plays a critical role in drug discovery. However, there is still a lack of targeted drug prediction for DNA-binding proteins. This study aims at the binding sites of DNA-binding proteins and drugs, by mining the residue interaction network features, which can describe the local and global structure of amino acids, combined with sequence feature. The predictor of DNA-binding protein–drug-binding sites is built by employing the Extreme Gradient Boosting (XGBoost) model with random under-sampling. We found that the residue interaction network features can better characterize DNA-binding proteins, and the binding sites with high betweenness value and high closeness value are more likely to interact with drugs. The model shows that the residue interaction network features can be used as an important quantitative indicator of drug-binding sites, and this method achieves high predictive performance for the binding sites of DNA-binding protein–drug. This study will help in drug discovery research for DNA-binding proteins.

Bioprospecting of microbial enzymes: current trends in industry and healthcare

Microbial enzymes have an indispensable role in producing foods, pharmaceuticals, and other commercial goods. Many novel enzymes have been reported from all domains of life, such as plants, microbes, and animals. Nonetheless, industrially desirable enzymes of microbial origin are limited. This review article discusses the classifications, applications, sources, and challenges of most demanded industrial enzymes such as pectinases, cellulase, lipase, and protease. In addition, the production of novel enzymes through protein engineering technologies such as directed evolution, rational, and de novo design, for the improvement of existing industrial enzymes is also explored. We have also explored the role of metagenomics, nanotechnology, OMICs, and machine learning approaches in the bioprospecting of novel enzymes. Overall, this review covers the basics of biocatalysts in industrial and healthcare applications and provides an overview of existing microbial enzyme optimization tools. KEY POINTS: • Microbial bioactive molecules are vital for therapeutic and industrial applications. • High-throughput OMIC is the most proficient approach for novel enzyme discovery. • Comprehensive databases and efficient machine learning models are the need of the hour to fast forward de novo enzyme design and discovery.

Dynamics of Post-Translational Modification Inspires Drug Design in the Kinase Family

Post-translational modification (PTM) on protein plays important roles in the regulation of cellular function and disease pathogenesis. The systematic analysis of PTM dynamics presents great opportunities to enlarge the target space by PTM allosteric regulation. Here, we presented a framework by integrating the sequence, structural topology, and particular dynamics features to characterize the functional context and druggabilities of PTMs in the well-known kinase family. The machine learning models with these biophysical features could successfully predict PTMs. On the other hand, PTMs were identified to be significantly enriched in the reported allosteric pockets and the allosteric potential of PTM pockets were thus proposed through these biophysical features. In the end, the covalent inhibitor DC-Srci-6668 targeting the PTM pocket in c-Src kinase was identified, which inhibited the phosphorylation and locked c-Src in the inactive state. Our findings represent a crucial step toward PTM-inspired drug design in the kinase family.

Computationally designed synthetic peptides for transporter proteins imparts allostericity in Miltefosine resistant L. major

The emergence of drug resistance is a major concern for combating against Cutaneous Leishmaniasis, a neglected tropical disease affecting 98 countries including India. Miltefosine is the only oral drug available for the disease and Miltefosine transporter proteins play a pivotal role in the emergence of drug-resistant Leishmania major. The cause of resistance is less accumulation of drug inside the parasite either by less uptake of the drug due to a decrease in the activity of P4ATPase-CDC50 complex or by increased efflux of the drug by P-glycoprotein (P-gp, an ABC transporter). In this paper, we are trying to allosterically modulate the behavior of resistant parasite (L. major) towards its sensitivity for the existing drug (Miltefosine, a phosphatidylcholine analog). We have used computational approaches to deal with the conservedness of the proteins and apparently its three-dimensional structure prediction through ab initio modeling. Long scale membrane-embedded molecular dynamics simulations were carried out to study the structural interaction and stability. Parasite-specific motifs of these proteins were identified based on the machine learning technique, against which a peptide library was designed. The protein-peptide docking shows good binding energy of peptides Pg5F, Pg8F and PC2 with specific binding to the motifs. These peptides were tested both in vitro and in vivo, where Pg5F in combination with PC2 showed 50-60% inhibition in resistant L. major's promastigote and amastigote forms and 80-90% decrease in parasite load in mice. We posit a model system wherein the data provide sufficient impetus for being novel therapeutics in order to counteract the drug resistance phenotype in Leishmania parasites.

The high catalytic rate of the cold-active Vibrio alkaline phosphatase requires a hydrogen bonding network involving a large interface loop

The role of surface loops in mediating communication through residue networks is still a relatively poorly understood part in the study of cold adaptation of enzymes, especially in terms of their quaternary interactions. Alkaline phosphatase (AP) from the psychrophilic marine bacterium Vibrio splendidus (VAP) is characterized by an analogous large surface loop in each monomer, referred to as the large loop, that hovers over the active site of the other monomer. It presumably has a role in the high catalytic efficiency of VAP which accompanies its extremely low thermal stability. Here, we designed several different variants of VAP with the aim of removing intersubunit interactions at the dimer interface. Breaking the intersubunit contacts from one residue in particular (Arg336) reduced the temperature stability of the catalytically potent conformation and caused a 40% drop in catalytic rate. The high catalytic rates of enzymes from cold‐adapted organisms are often associated with increased dynamic flexibility. Comparison of the relative B‐factors of the R336L crystal structure to that of the wild‐type confirmed surface flexibility was increased in a loop on the opposite monomer, but not in the large loop. The increase in flexibility resulted in a reduced catalytic rate. The large loop increases the area of the interface between the subunits through its contacts and may facilitate an alternating structural cycle demanded by a half‐of‐sites reaction mechanism through stronger ties, as the dimer oscillates between high affinity (active) or low phosphoryl group affinity (inactive).

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.

Both intra and inter-domain interactions define the intrinsic dynamics and allosteric mechanism in DNMT1s

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Dynamics and allosteric potentials of the RFTS domain are proposed.

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Hinge sites located at the RFTS-CD interface are key regulators for inter-domain interactions.

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Network analysis reveals local allosteric networks and inter-domain communication pathways in DNMT1.

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A potential allosteric site at the TRD interface for DNMT1 is identified.

DNA methyltransferase 1 (DNMT1), a large multidomain enzyme, is believed to be involved in the passive transmission of genomic methylation patterns via methylation maintenance. Yet, the molecular mechanism of interaction networks underlying DNMT1 structures, dynamics, and its biological significance has yet to be fully characterized. In this work, we used an integrated computational strategy that combined coarse-grained and atomistic simulations with coevolution information and network modeling of the residue interactions for the systematic investigation of allosteric dynamics in DNMT1. The elastic network modeling has proposed that the high plasticity of RFTS has strengthened the correlated behaviors of DNMT1 structures through the hinge sites located at the RFTS-CD interface, which mediate the collective motions between domains. The perturbation response scanning (PRS) analysis combined with the enrichment analysis of disease mutations have further highlighted the allosteric potential of the RFTS domain. Furthermore, the long-range paths connect the intra-domain interactions through the TRD interface and catalytic interface, emphasizing some key inter-domain interactions as the bridges in the global allosteric regulation of DNMT1. The observed interplay between conserved intra-domain networks and dynamical plasticity encoded by inter-domain interactions provides insights into the intrinsic dynamics and functional evolution, as well as the design of allosteric modulators of DNMT1 based on the TRD interface.

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.