A New Beginning to the Existing Medicines; Repurposing FDA-Approved Drugs for the Neglected Re-Emerging Disease Leptospirosis.

Leptospirosis is one of the re-emerging zoonotic diseases, especially in tropical regions. Many antibiotics are used to treat leptospirosis, but there are no scientific evidence-based guidelines or systematic clinical trials for using these drugs. A bioinformatics approach was made to shortlist some Food and Drug Administration (FDA) of the United States of America-approved and currently used drugs for leptospirosis. The existing drugs from the Drug Bank database, which are currently not used for leptospirosis, were selected to identify their target proteins and binding sites using bioinformatics methods. Orthologues of these target proteins were selected from the proteome database of Leptospira. The similar sites and their interactions with the drugs were validated and recommended for use in leptospirosis. Further, the sensitivity of recommended drugs was also validated in vitro. The sequences and structures of these proteins were compared under strictly controlled parameters and shortlisted Gatifloxacin, Imipenem, Latamoxef, Doripenem, Tigecycline, and Lactams as repurposable drugs for leptospirosis. An in vitro validation of the drugs showed significant antileptospiral activity in 12 serovars with low IC50 concentrations and also showed that the IC50 values varied across Leptospira serovars. Further, suitable proteins under the concept of "One Target, Many Drugs" identified DNA gyrase subunit A (Q72WD1), 30S ribosomal protein S9 (Q72U99), and 30S ribosomal protein S12 (Q72UA6), and these proteins were found across the pathogenic, saprophytic, and intermediate species of Leptospira. We describe a method to find repurposable drugs from the approved list that are not currently used to treat leptospirosis and validate them to be taken forward for systematic clinical trials specific to leptospirosis for recommendations in clinical use.

Normal Mode Analysis Elicits Conformational Shifts in Proteins at Both Proximal and Distal Regions to the Phosphosite Stemming from Single-Site Phosphorylation.

Phosphorylation, a fundamental biochemical switch, intricately regulates protein function and signaling pathways. Our study employs extensive computational structural analyses on a curated data set of phosphorylated and unphosphorylated protein pairs to explore the multifaceted impact of phosphorylation on protein conformation. Using normal mode analysis (NMA), we investigated changes in protein flexibility post-phosphorylation, highlighting an enhanced level of structural dynamism. Our findings reveal that phosphorylation induces not only local changes at the phosphorylation site but also extensive alterations in distant regions, showcasing its far-reaching influence on protein structure-dynamics. Through in-depth case studies on polyubiquitin B and glycogen synthase kinase-3 beta, we elucidate how phosphorylation at distinct sites leads to variable structural and dynamic modifications, potentially dictating functional outcomes. While phosphorylation largely preserves the residue motion correlation, it significantly disrupts low-frequency global modes, presenting a dualistic impact on protein dynamics. We also explored alterations in the total accessible surface area (ASA), emphasizing region-specific changes around phosphorylation sites. This study sheds light on phosphorylation-induced conformational changes, dynamic modulation, and surface accessibility alterations, leveraging an integrated computational approach with RMSD, NMA, and ASA, thereby contributing to a comprehensive understanding of cellular regulation and suggesting promising avenues for therapeutic interventions.

Interfacial residues in protein-protein complexes are in the eyes of the beholder.

Interactions between proteins are vital in almost all biological processes. The characterization of protein-protein interactions helps us understand the mechanistic basis of biological processes, thereby enabling the manipulation of proteins for biotechnological and clinical purposes. The interface residues of a protein-protein complex are assumed to have the following two properties: (a) they always interact with a residue of a partner protein, which forms the basis for distance-based interface residue identification methods, and (b) they are solvent-exposed in the isolated form of the protein and become buried in the complex form, which forms the basis for Accessible Surface Area (ASA)-based methods. The study interrogates this popular assumption by recognizing interface residues in protein-protein complexes through these two methods. The results show that a few residues are identified uniquely by each method, and the extent of conservation, propensities, and their contribution to the stability of protein-protein interaction varies substantially between these residues. The case study analyses showed that interface residues, unique to distance, participate in crucial interactions that hold the proteins together, whereas the interface residues unique to the ASA method have a potential role in the recognition, dynamics, and specificity of the complex and can also be a hotspot. Overall, the study recommends applying both distance and ASA methods so that some interface residues missed by either method but crucial to the stability, recognition, dynamics, and function of protein-protein complexes are identified in a complementary manner.

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.

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.

Comparative analysis of permanent and transient domain-domain interactions in multi-domain proteins.

Protein domains are structural, functional, and evolutionary units. These domains bring out the diversity of functionality by means of interactions with other co-existing domains and provide stability. Hence, it is important to study intra-protein inter-domain interactions from the perspective of types of interactions. Domains within a chain could interact over short timeframes or permanently, rather like protein-protein interactions (PPIs). However, no systematic study has been carried out between two classes, namely permanent and transient domain-domain interactions. In this work, we studied 263 two-domain proteins, belonging to either of these classes and their interfaces on the basis of several factors, such as interface area and details of interactions (number, strength, and types of interactions). We also characterized them based on residue conservation at the interface, correlation of residue motions across domains, its involvement in repeat formation, and their involvement in particular molecular processes. Finally, we could analyze the interactions arising from domains in two-domain monomeric proteins, and we observed significant differences between these two classes of domain interactions and a few similarities. This study will help to obtain a better understanding of structure-function and folding principles of multi-domain proteins.

Computational tools to study RNA-protein complexes.

RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein-RNA interactions are still poorly derstood in contrast to protein-protein and protein-DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.

Structures of distantly related interacting protein homologs are less divergent than non-interacting homologs.

Homologous proteins can display high structural variation due to evolutionary divergence at low sequence identity. This classical inverse relationship between sequence identity and structural similarity, established many years ago, has remained true between homologous proteins of known structure over time. However, a large number of heteromeric proteins also exist in the structural data bank, where the interacting subunits belong to the same fold and maintain low sequence identity between themselves. It is not clear if there is any selection pressure to deviate from the inverse sequence-structure relationship for such interacting distant homologs, in comparison to pairs of homologs which are not known to interact. We examined 12,824 fold pairs of interacting homologs of known structure, which includes both heteromers and multi-domain proteins. These were compared with monomeric proteins, resulting in 26,082 fold pairs as a dataset of non-interacting homologous systems. Interacting homologs were found to retain higher structural similarity than non-interacting homologs at diminishing sequence identity in a statistically significant manner. Interacting homologs are more similar in their 3D structures than non-interacting homologs and have a preference towards symmetric association. There appears to be a structural constraint between remote homologs due to this commitment.

Computational Study on the Dynamics of Mycobacterium Tuberculosis RNA Polymerase Assembly.

Gene regulation is an intricate phenomenon involving precise function of many macromolecular complexes. Molecular basis of this phenomenon is highly complex and cannot be fully understood using a single technique. Computational approaches can play a crucial role in overall understanding of functional and mechanistic features of a protein or an assembly. Large amounts of structural data pertaining to these complexes are publicly available. In this project, we took advantage of the availability of the structural information to unravel functional intricacies of Mycobacterium tuberculosis RNA polymerase upon interaction with RbpA. In this article, we discuss how the knowledge on protein structure and dynamics can be exploited to study function using various computational tools and resources. Overall, this article provides an overview of various computational methods which can be efficiently used to understand the role of any protein. We hope especially the nonexperts in the field could benefit from our article.

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.

Profiles of Natural and Designed Protein-Like Sequences Effectively Bridge Protein Sequence Gaps: Implications in Distant Homology Detection.

Sequence-based approaches are fundamental to guide experimental investigations in obtaining structural and/or functional insights into uncharacterized protein families. Powerful profile-based sequence search methods rely on a sequence space continuum to identify non-trivial relationships through homology detection. The computational design of protein-like sequences that serve as "artificial linkers" is useful in identifying relationships between distant members of a structural fold. Such sequences act as intermediates and guide homology searches between distantly related proteins. Here, we describe an approach that represents natural intermediate sequences and designed protein-like sequences as HMM (Hidden Markov Models) profiles, to improve the sensitivity of existing search methods. Searches made within the "Profile database" were shown to recognize the parent structural fold for 90% of the search queries at query coverage better than 60%. For 1040 protein families with no available structure, fold associations were made through searches in the database of natural and designed sequence profiles. Most of the associations were made with the Alpha-alpha superhelix, Transmembrane beta-barrels, TIM barrel, and Immunoglobulin-like beta-sandwich folds. For 11 domain families of unknown functions, we provide confident fold associations using the profiles of designed sequences and a consensus from other fold recognition methods. For two DUFs (Domain families of Unknown Functions), we performed detailed functional annotation through comparisons with characterized templates of families of known function.

DSDBASE 2.0: updated version of DiSulphide dataBASE, a database on disulphide bonds in proteins.

Disulphide bonds are stabilizing crosslinks in proteins and serve to enhance their thermal stability. In proteins that are small and rich in disulphide bonds, they could be the major determining factor for the choice of conformational state since their constraints on appropriate backbone conformation can be substantial. Such crosslinks and their positional conservation could itself enable protein family and functional association. Despite the importance of the field, there is no comprehensive database on disulphide crosslinks that is available to the public. Herein we provide information on disulphides in DSDBASE2.0, an updated and significantly expanded database that is freely available, fully annotated and manually curated database on native and modelled disulphides. The web interface also provides several useful computational tools that have been specifically developed for proteins containing disulphide crosslinks. The modelling of disulphide crosslinks is performed using stereochemical criteria, coded within our Modelling of Disulphides in Proteins (MODIP) algorithm. The inclusion of modelled disulphides potentially enhances the loop database substantially, thereby permitting the recognition of compatible polypeptide segments that could serve as templates for immediate modelling. The DSDBASE2.0 database has been updated to include 153,944 PDB entries, 216,096 native and 20,153,850 modelled disulphide bond segments from PDB January 2021 release. The current database also provides a resource to user-friendly search for multiple disulphide bond containing loops, along with annotation of their function using GO and subcellular localization of the query. Furthermore, it is possible to obtain the three-dimensional models of disulphide-rich small proteins using an independent algorithm, RANMOD, that generates and examines random, but allowed backbone conformations of the polypeptide. DSDBASE2.0 still remains the largest open-access repository that organizes all disulphide bonds of proteins on a single platform. The database can be accessed from http://caps.ncbs.res.in/dsdbase2.

Comparison of side-chain dispersion in protein structures determined by cryo-EM and X-ray crystallography.

An evaluation of systematic differences in local structure and conformation in the interior of protein tertiary structures determined by crystallography and by cryo-electron microscopy (cryo-EM) is reported. The expectation is that any consistent differences between the derived atomic models could provide insights into variations in side-chain packing that result from differences in specimens prepared for analysis between these two methods. By computing an atomic packing score, which provides a quantitative measure of clustering of side-chain atoms in the core of the tertiary structures, it is found that, in general, for structures determined by cryo-EM, side chains are more dispersed than in structures determined by X-ray crystallography over a similar resolution range. This trend is also observed in the packing comparison at subunit interfaces. Similar trends were observed in the packing comparison at the core of tertiary structures of the same proteins determined by both X-ray and cryo-EM methods. It is proposed here that the reduced dispersion of side chains in protein crystals could be due to some level of dehydration in 3D crystals prepared for X-ray crystallography and also because the higher rate of freezing of protein samples for cryo-EM may enable preservation of a more native conformation.

Identification, functional characterization, assembly and structure of ToxIN type III toxin-antitoxin complex from E. coli.

Toxin-antitoxin (TA) systems are proposed to play crucial roles in bacterial growth under stress conditions such as phage infection. The type III TA systems consist of a protein toxin whose activity is inhibited by a noncoding RNA antitoxin. The toxin is an endoribonuclease, while the antitoxin consists of multiple repeats of RNA. The toxin assembles with the individual antitoxin repeats into a cyclic complex in which the antitoxin forms a pseudoknot structure. While structure and functions of some type III TA systems are characterized, the complex assembly process is not well understood. Using bioinformatics analysis, we have identified type III TA systems belonging to the ToxIN family across different Escherichia coli strains and found them to be clustered into at least five distinct clusters. Furthermore, we report a 2.097 Å resolution crystal structure of the first E. coli ToxIN complex that revealed the overall assembly of the protein-RNA complex. Isothermal titration calorimetry experiments showed that toxin forms a high-affinity complex with antitoxin RNA resulting from two independent (5' and 3' sides of RNA) RNA binding sites on the protein. These results further our understanding of the assembly of type III TA complexes in bacteria.

Pseudokinases repurpose flexibility signatures associated with the protein kinase fold for noncatalytic roles.

The bilobal protein kinase-like fold in pseudokinases lack one or more catalytic residues, conserved in canonical protein kinases, and are considered enzymatically deficient. Tertiary structures of pseudokinases reveal that their loops topologically equivalent to activation segments of kinases adopt contracted configurations, which is typically extended in active conformation of kinases. Herein, anisotropic network model based normal mode analysis (NMA) was conducted on 51 active conformation structures of protein kinases and 26 crystal structures of pseudokinases. Our observations indicate that although backbone fluctuation profiles are similar for individual kinase-pseudokinase families, low intensity mean square fluctuations in pseudo-activation segment and other sub-structures impart rigidity to pseudokinases. Analyses of collective motions from functional modes reveal that pseudokinases, compared to active kinases, undergo distinct conformational transitions using the same structural fold. All-atom NMA of protein kinase-pseudokinase pairs from each family, sharing high amino acid sequence identities, yielded distinct community clusters, partitioned by residues exhibiting highly correlated fluctuations. It appears that atomic fluctuations from equivalent activation segments guide community membership and network topologies for respective kinase and pseudokinase. Our findings indicate that such adaptations in backbone and side-chain fluctuations render pseudokinases competent for catalysis-independent roles.

Shaking the ß-Bulges.

ß-bulges are irregularities inside the ß-sheets. They represent more than 3 percent of the protein residues, i.e., they are as frequent as 3.10 helices. In terms of evolution, ß-bulges are not more conserved than any other local protein conformations within homologous protein structures. In a first of its kind study, we have investigated the dynamical behaviour of ß-bulges using the largest known set of protein molecular dynamics simulations. We observed that more than 50 percent of the existing ß-bulges in protein crystal structures remained stable during dynamics while more than1/6th were not stable at all and disappeared entirely. Surprisingly, 1.1 percent of ß-bulges that appeared remained stable. ß-bulges have been categorized in different subtypes. The most common ß-bulges' types are the smallest insertion in ß-strands (namely AC and AG); they are found as stable as the whole ß-bulges dataset. Low occurring types (namely PC and AS), that have the largest insertions, are significantly more stable than expected. Thus, this pioneer study allowed to precisely quantify the stability of the ß-bulges, demonstrating their structural robustness, with few unexpected cases raising structural questions.

Hypervariability of accessible and inaccessible conformational space of proteins.

Proteins undergo motions in a range of amplitudes, from domain motions to backbone rotations, leading to changes in (φ,ψ) torsion angles and small-scale bond vibrations and angle bending. Here, we study the extent of variations in (φ,ψ) values in proteins and the effects of bond geometry variations due to vibrational motions in a protein on the accessible, (steric clash-free) (φ,ψ) space. We perform 1-fs timestep unconstrained molecular dynamics simulations on super-high-resolution protein structures. Extent of variations in bond geometry during the simulation is within acceptable ranges of bond lengths and angles. However, the steric clash-free (φ,ψ) space continuously changes as seen in bond geometry-specific (φ,ψ) steric maps at the residue level during simulations. (φ,ψ) regions that have steric clash at one timepoint can become steric clash-free at a different timepoint through minor adjustments to backbone bond lengths and angles. Also instances of (φ,ψ) transitions from the left to right half of the (φ,ψ) map in consecutive snapshots of the trajectory are seen. Although the two quadrants are separated by a steric clash-prone region, corresponding to a high-energy barrier, height of this barrier is lowered by adjusting the bond geometry such that a bridging region of steric clash-free, low-energy (φ,ψ) values is formed. We demonstrate the idea of dynamically varying nature of acceptable and accessible (φ,ψ) steric space in proteins, which has implications for protein folding; proteins could sample (φ,ψ) space which is originally considered to be inaccessible, during folding, through minor adjustments to their backbone bond geometry.

Comparative Analysis of Structural and Dynamical Features of Ribosome Upon Association With mRNA Reveals Potential Role of Ribosomal Proteins.

Ribosomes play a critical role in maintaining cellular proteostasis. The binding of messenger RNA (mRNA) to the ribosome regulates kinetics of protein synthesis. To generate an understanding of the structural, mechanistic, and dynamical features of mRNA recognition in the ribosome, we have analysed mRNA-protein interactions through a structural comparison of the ribosomal complex in the presence and absence of mRNA. To do so, we compared the 3-Dimensional (3D) structures of components of the two assembly structures and analysed their structural differences because of mRNA binding, using elastic network models and structural network-based analysis. We observe that the head region of 30S ribosomal subunit undergoes structural displacement and subunit rearrangement to accommodate incoming mRNA. We find that these changes are observed in proteins that lie far from the mRNA-protein interface, implying allostery. Further, through perturbation response scanning, we show that the proteins S13, S19, and S20 act as universal sensors that are sensitive to changes in the inter protein network, upon binding of 30S complex with mRNA and other initiation factors. Our study highlights the significance of mRNA binding in the ribosome complex and identifies putative allosteric sites corresponding to alterations in structure and/or dynamics, in regions away from mRNA binding sites in the complex. Overall, our work provides fresh insights into mRNA association with the ribosome, highlighting changes in the interactions and dynamics of the ribosome assembly because of the binding.

'All That Glitters Is Not Gold': High-Resolution Crystal Structures of Ligand-Protein Complexes Need Not Always Represent Confident Binding Poses.

Our understanding of the structure-function relationships of biomolecules and thereby applying it to drug discovery programs are substantially dependent on the availability of the structural information of ligand-protein complexes. However, the correct interpretation of the electron density of a small molecule bound to a crystal structure of a macromolecule is not trivial. Our analysis involving quality assessment of ~0.28 million small molecule-protein binding site pairs derived from crystal structures corresponding to ~66,000 PDB entries indicates that the majority (65%) of the pairs might need little (54%) or no (11%) attention. Out of the remaining 35% of pairs that need attention, 11% of the pairs (including structures with high/moderate resolution) pose serious concerns. Unfortunately, most users of crystal structures lack the training to evaluate the quality of a crystal structure against its experimental data and, in general, rely on the resolution as a 'gold standard' quality metric. Our work aims to sensitize the non-crystallographers that resolution, which is a global quality metric, need not be an accurate indicator of local structural quality. In this article, we demonstrate the use of several freely available tools that quantify local structural quality and are easy to use from a non-crystallographer's perspective. We further propose a few solutions for consideration by the scientific community to promote quality research in structural biology and applied areas.

NOD: a web server to predict New use of Old Drugs to facilitate drug repurposing.

Computational methods accelerate the drug repurposing pipelines that are a quicker and cost-effective alternative to discovering new molecules. However, there is a paucity of web servers to conduct fast, focussed, and customized investigations for identifying new uses of old drugs. We present the NOD web server, which has the mentioned characteristics. NOD uses a sensitive sequence-guided approach to identify close and distant homologs of a protein of interest. NOD then exploits this evolutionary information to suggest potential compounds from the DrugBank database that can be repurposed against the input protein. NOD also allows expansion of the chemical space of the potential candidates through similarity searches. We have validated the performance of NOD against available experimental and/or clinical reports. In 65.6% of the investigated cases in a control study, NOD is able to identify drugs more effectively than the searches made in DrugBank. NOD is freely-available at http://pauling.mbu.iisc.ac.in/NOD/NOD/ .