CarbDisMut: database on neutral and disease-causing mutations in human carbohydrate-binding proteins.

Protein-carbohydrate interactions are involved in several cellular and biological functions. Integrating structure and function of carbohydrate-binding proteins with disease-causing mutations help to understand the molecular basis of diseases. Although databases are available for protein-carbohydrate complexes based on structure, binding affinity and function, no specific database for mutations in human carbohydrate-binding proteins is reported in the literature. We have developed a novel database, CarbDisMut, a comprehensive integrated resource for disease-causing mutations with sequence and structural features. It has 1.17 million disease-associated mutations and 38,636 neutral mutations from 7,187 human carbohydrate-binding proteins. The database is freely available at https://web.iitm.ac.in/bioinfo2/carbdismut. The web-site is implemented using HTML, PHP and JavaScript and supports recent versions of all major browsers, such as Firefox, Chrome and Opera.

TMH Stab-pred: Predicting the stability of α-helical membrane proteins using sequence and structural features.

The folding and stability of transmembrane proteins (TMPs) are governed by the insertion of secondary structural elements into the cell membrane followed by their assembly. Understanding the important features that dictate the stability of TMPs is important for elucidating their functions. In this work, we related sequence and structure-based parameters with free energy (ΔG0) of α-helical membrane proteins. Our results showed that the free energy transfer of hydrophobic peptides, relative contact order, total interaction energy, number of hydrogen bonds and lipid accessibility of transmembrane regions are important for stability. Further, we have developed multiple-regression models to predict the stability of α-helical membrane proteins using these features and our method can predict the stability with a correlation and mean absolute error (MAE) of 0.89 and 1.21 kcal/mol, respectively, on jack-knife test. The method was validated with a blind test set of three recently reported experimental ΔG0, which could predict the stability within an average MAE of 0.51 kcal/mol. Further, we developed a webserver for predicting the stability and it is freely available at (https://web.iitm.ac.in/bioinfo2/TMHS/). The importance of selected parameters and limitations are discussed.

MPTherm: database for membrane protein thermodynamics for understanding folding and stability.

The functions of membrane proteins (MPs) are attributed to their structure and stability. Factors influencing the stability of MPs differ from globular proteins due to the presence of membrane spanning regions. Thermodynamic data of MPs aid to understand the relationship among their structure, stability and function. Although a wealth of experimental data on thermodynamics of MPs are reported in the literature, there is no database available explicitly for MPs. In this work, we have developed a database for MP thermodynamics, MPTherm, which contains more than 7000 thermodynamic data from about 320 MPs. Each entry contains protein sequence and structural information, membrane topology, experimental conditions, thermodynamic parameters such as melting temperature, free energy, enthalpy etc. and literature information. MPTherm assists users to retrieve the data by using different search and display options. We have also provided the sequence and structure visualization as well as cross-links to UniProt and PDB databases. MPTherm database is freely available at http://www.iitm.ac.in/bioinfo/mptherm/. It is implemented in HTML, PHP, MySQL and JavaScript, and supports the latest versions of major browsers, such as Firefox, Chrome and Opera. MPTherm would serve as an effective resource for understanding the stability of MPs, development of prediction tools and identifying drug targets for diseases associated with MPs.

Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins.

Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.

ProThermDB: thermodynamic database for proteins and mutants revisited after 15 years.

ProThermDB is an updated version of the thermodynamic database for proteins and mutants (ProTherm), which has ∼31 500 data on protein stability, an increase of 84% from the previous version. It contains several thermodynamic parameters such as melting temperature, free energy obtained with thermal and denaturant denaturation, enthalpy change and heat capacity change along with experimental methods and conditions, sequence, structure and literature information. Besides, the current version of the database includes about 120 000 thermodynamic data obtained for different organisms and cell lines, which are determined by recent high throughput proteomics techniques using whole-cell approaches. In addition, we provided a graphical interface for visualization of mutations at sequence and structure levels. ProThermDB is cross-linked with other relevant databases, PDB, UniProt, PubMed etc. It is freely available at https://web.iitm.ac.in/bioinfo2/prothermdb/index.html without any login requirements. It is implemented in Python, HTML and JavaScript, and supports the latest versions of major browsers, such as Firefox, Chrome and Safari.

Pred-MutHTP: Prediction of disease-causing and neutral mutations in human transmembrane proteins.

Membrane proteins are unique in that segments thereof concurrently reside in vastly different physicochemical environments: the extracellular space, the lipid bilayer, and the cytoplasm. Accordingly, the effects of missense variants disrupting their sequence depend greatly on the characteristics of the environment of the protein segment affected as well as the function it performs. Because membrane proteins have many crucial roles (transport, signal transduction, cell adhesion, etc.), compromising their functionality often leads to diseases including cancers, diabetes mellitus or cystic fibrosis. Here, we report a suite of sequence-based computational methods "Pred-MutHTP" for discriminating between disease-causing and neutral alterations in their sequence. With a data set of 11,846 disease-causing and 9,533 neutral mutations, we obtained an accuracy of 74% and 78% with 10-fold group-wise cross-validation and test set, respectively. The features used in the models include evolutionary information, physiochemical properties, neighboring residue information, and specialized membrane protein attributes incorporating the number of transmembrane segments, substitution matrices specific to membrane proteins as well as residue distributions occurring in specific topological regions. Across 11 disease classes, the method achieved accuracies in the range of 75-85%. The model designed specifically for the transmembrane segments achieved an accuracy of 85% on the test set with a sensitivity and specificity of 86% and 83%, respectively. This renders our method the current state-of-the-art with regard to predicting the effects of variants in the transmembrane protein segments. Pred-MutHTP allows predicting the effect of any variant occurring in a membrane protein-available at https://www.iitm.ac.in/bioinfo/PredMutHTP/.

Statistical analysis of disease-causing and neutral mutations in human membrane proteins.

Mutations in transmembrane proteins (TMPs) have diverse effects on their structure and functions, which may lead to various diseases. In this present study, we have investigated variations in human membrane proteins and found that negatively charged to positively charged/polar and nonpolar to nonpolar changes are dominant in disease-causing and neutral mutations, respectively. Further, we analyzed the top 10 preferred mutations in 14 different disease classes and found that each class has at least two Arg mutations. Moreover, in cardiovascular diseases and congenital disorders of metabolism, Cys mutations occur more frequently in single-pass proteins, whereas Arg and nonpolar residues are more frequently substituted in multi-pass membrane proteins. The immune system diseases are enriched in C → R and C → Y mutations in inside and outside regions. On the other hand, in the membrane region, E → K and R → Q mutations are prevalent. The comparison of mutations in topologically similar regions of globular and membrane proteins showed that Ser and Thr mutations cause deleterious effects in membrane regions, whereas Cys and charged residues, Asp and Arg are prevalent in the buried regions of globular proteins. Our comprehensive analysis of disease-associated mutations in transmembrane proteins will be useful for developing prediction tools.

MutHTP: mutations in human transmembrane proteins.

Motivation: Existing sources of experimental mutation data do not consider the structural environment of amino acid substitutions and distinguish between soluble and membrane proteins. They also suffer from a number of further limitations, including data redundancy, lack of disease classification, incompatible information content, and ambiguous annotations (e.g. the same mutation being annotated as disease and benign). Results: We have developed a novel database, MutHTP, which contains information on 183 395 disease-associated and 17 827 neutral mutations in human transmembrane proteins. For each mutation site MutHTP provides a description of its location with respect to the membrane protein topology, structural environment (if available) and functional features. Comprehensive visualization, search, display and download options are available. Availability and implementation: The database is publicly available at http://www.iitm.ac.in/bioinfo/MutHTP/. The website is implemented using HTML, PHP and javascript and supports recent versions of all major browsers, such as Firefox, Chrome and Opera. Supplementary information: Supplementary data are available at Bioinformatics online.

Dissecting and analyzing key residues in protein-DNA complexes.

Protein-DNA interactions are involved in various fundamental biological processes such as replication, transcription, DNA repair, and gene regulation. To understand the interaction in protein-DNA complexes, the integrative study of binding and stabilizing residues is important. In the present study, we have identified key residues that play a dual role in both binding and stability from a nonredundant dataset of 319 protein-DNA complexes. We observed that key residues are identified in very less number of complexes (29%) and only about 4% of stabilizing/binding residues are identified as key residues. Specifically, stabilizing residues have higher preference to be key residues than binding residues. These key residues include polar, nonpolar, aliphatic, aromatic, and charged amino acids. Moreover, we have analyzed and discussed the key residues in different protein-DNA complexes, which are classified based on protein structural class, function, DNA strand, and their conformations. Especially, Ser, Thr, Tyr, Arg, and Lys residues are commonly found in most of the subclasses of protein-DNA complexes. Further, we analyzed atomic contacts, which show that polar-nonpolar is more enriched than other types of contacts. In addition, the charged contacts are highly preferred in protein-DNA complexes compared with protein-protein and protein-RNA complexes. Finally, we have discussed the sequence and structural features of key residues such as conservation score, surrounding hydrophobicity, solvent accessibility, secondary structure, and long-range order. This study will be helpful to understand the recognition mechanism and structural and functional aspects of protein-DNA complexes.

Alz-Disc: A Tool to Discriminate Disease-causing and Neutral Mutations in Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) is the most common neurodegenerative disorder that affects the neuronal system and leads to memory loss. Many coding gene variants are associated with this disease and it is important to characterize their annotations. METHODS: We collected the Alzheimer's disease-causing and neutral mutations from different databases. For each mutation, we computed the different features from protein sequence. Further, these features were used to build a Bayes network-based machine-learning algorithm to discriminate between the disease-causing and neutral mutations in AD. RESULTS: We have constructed a comprehensive dataset of 314 Alzheimer's disease-causing and 370 neutral mutations and explored their characteristic features such as conservation scores, positionspecific scoring matrix (PSSM) profile, and the change in hydrophobicity, different amino acid residue substitution matrices and neighboring residue information for identifying the disease-causing mutations. Utilizing these features, we have developed a disease-specific tool named Alz-disc, for discriminating the disease-causing and neutral mutations using sequence information alone. The performance of the present method showed an accuracy of 89% for independent test set, which is 13% higher than available generic methods. This method is freely available as a web server at https://web.iitm.ac.in/bioinfo2/alzdisc/. CONCLUSIONS: This study is useful to annotate the effect of new variants and develop mutation specific drug design strategies for Alzheimer's disease.

MPAD: A Database for Binding Affinity of Membrane Protein-protein Complexes and their Mutants.

Membrane protein complexes are crucial for a large variety of biological functions which are mainly dictated by their binding affinity. Due to the intricate nature of their structure, however, the binding affinity of membrane proteins is less explored compared to globular proteins. Mutations in these complexes affect their binding affinity, as well as impair critical functions, and may lead to diseases. Although experimental binding affinity data have expanded in the literature, they are dispersed and it is necessary to compile them into a reliable and comprehensive database. Hence, we developed MPAD (Membrane Protein complex binding Affinity Database), which contains experimental binding affinities of membrane protein-protein complexes and their mutants along with sequence, structure, and functional information, membrane-specific features, experimental conditions, as well as literature information. MPAD has an easy-to-use interface and options to build search queries, display, sort, download, and upload the data are among the other features available to users. This database can be used to understand the factors influencing the binding affinity in membrane proteins when compared to globular proteins as well as the impact of mutations on binding affinity, which may have potential applications to structure-based drug design. MPAD can be freely accessed at https://web.iitm.ac.in/bioinfo2/mpad.

Synthesis, biological-pharmacological evaluation and molecular docking studies of Schiff base analogues of transition metal (II) complexes of 4-amino-1,5-dimethyl-2-phenylpyrazol-3-one.

Novel tridentate Schiff base [CuL2], [NiL2], [CoL2], [MnL2] and [ZnL2] complexes have been prepared with Schiff base resulting from acetophenylidene-4-iminoantipyrine and tyrosine. Microanalytical data, IR, UV-vis, 1H, 13C-NMR, powder XRD, SEM, cyclic voltammetry, ESR, and mass spectral techniques confirmed the structural features of the chelates. The general formula of the complexes [ML2] was confirmed from elemental analysis, mass and 1H-NMR spectral studies. Octahedral geometry of the chelates is confirmed by electronic absorption spectra and FT-IR spectra. The magnetic susceptibility and low conductance values reveal that the complexes are monomeric and non-electrolytic nature, respectively. Powder XRD and SEM images confirm the crystalline structure of the complexes. At 300 and 77 K, the X-band ESR spectra of [CuL2] complex in DMSO solution were recorded and their salient features have been reported. The binding of [CuL2] with CT-DNA study reveals that interactions occur through intercalation. Analgesic, anti-inflammatory and CNS activities and antimicrobial activities of Schiff base and its complexes reveal that the chelates have higher potent than free ligand. The molecular docking studies have been performed with DNA and 6COX enzyme using Hex 8.0 software which recognizes the biological activities and nature of binding of the complexes.Communicated by Ramaswamy H. Sarma.

Exploring Plausible Therapeutic Targets for Alzheimer's Disease using Multi-omics Approach, Machine Learning and Docking.

The progressive deterioration of neurons leads to Alzheimer's disease (AD), and developing a drug for this disorder is challenging. Substantial gene/transcriptome variability from multiple cell types leads to downstream pathophysiologic consequences that represent the heterogeneity of this disease. Identifying potential biomarkers for promising therapeutics is strenuous due to the fact that the transcriptome, epigenetic, or proteome changes detected in patients are not clear whether they are the cause or consequence of the disease, which eventually makes the drug discovery efforts intricate. The advancement in scRNA-sequencing technologies helps to identify cell type-specific biomarkers that may guide the selection of the pathways and related targets specific to different stages of the disease progression. This review is focussed on the analysis of multi-omics data from various perspectives (genomic and transcriptomic variants, and single-cell expression), which provide insights to identify plausible molecular targets to combat this complex disease. Further, we briefly outlined the developments in machine learning techniques to prioritize the risk-associated genes, predict probable mutations and identify promising drug candidates from natural products.

Synthesis, structural, pharmacological and molecular docking simulations studies of Schiff base transition metal complexes procured from acetylacetonyl-4-iminoantipyrine and tyrosine.

Schiff base complexes of Cu(II), Ni(II), Co(II), VO(II) and Zn(II) ions have been synthesized by condensation of acetylaceto-4-iminoantipyrine and tyrosine. The structural characterization of compounds has been investigated using elemental analysis, molar conductance and magnetic susceptibility measurements, UV-Vis., FT-IR, ESI-mass, 1H-NMR, 13C-NMR, ESR spectroscopy, cyclic voltammetry, XRD and SEM techniques. The observed analytical data indicate that the metal chelates have the general formula of [ML] type. Powder XRD pattern of Schiff base and its complexes authenticate their crystalline nature. From SEM morphology studies, the reduction grain size of Schiff base in metal chelates was observed which is due to the chelation of the Schiff base. From the IR, electronic absorption spectra and magnetic measurement data, square planar geometry was proposed for the metal complexes except for the [VOL] complex which exhibits square pyramidal geometry. The X-band ESR spectra of [CuL] and [VOL] complexes in DMSO solution were recorded at 77 & 300 K and their spin Hamiltonian parameter values support the proposed geometry. The interactions of the [CuL] complex with CT-DNA were investigated by UV-Vis and absorption titrations. DNA binding interaction studies reveal the hydrophobic interaction between CT-DNA and complexes. Furthermore, analgesic, anti-inflammatory, CNS and antimicrobial studies of the Schiff base and its transition metal complexes were examined and resolved metal complexes have enhanced pharmacological and biological activity when compared to free Schiff base. Molecular docking studies of the complex with DNA and (PDB ID: 6COX) protein were investigated.

Illustrative Tutorials for ProThermDB: Thermodynamic Database for Proteins and Mutants.

ProThermDB (https://web.iitm.ac.in/bioinfo2/prothermdb/index.html) is a primary resource for protein stability, which contains experimentally determined thermodynamic data for proteins and their mutants. The most recent version of ProThermDB accumulates the data obtained from both high- and low-throughput experimental biophysical methods. It includes comprehensive information at four different levels, i.e.: (i) protein sequence and structure; (ii) experimental conditions; (iii) thermodynamic parameters such as Gibbs free energy, melting temperature, enthalpy, etc.; and (iv) literature. In the following protocols, we present detailed tutorials for retrieving data using different search, display and sorting options, interpretation of search results, description of each entry-level information category, data upload and download, cross-links with other databases, and visualization options. This protocol consists of six pictorial exercises, which are useful for biologists/users to understand the contents and organization of data in ProThermDB. Further, potential applications of ProThermDB in protein engineering are discussed. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Retrieval of experimental thermodynamic data for wild-type and mutants of a specific protein using a simple query Basic Protocol 2: Retrieval of stabilizing point mutations, which are located at the interior of α-helical regions, and obtaining data by thermal denaturation methods Basic Protocol 3: Retrieval of destabilizing point mutations, which are in ß-sheets of exposed regions, and obtaining data by chemical denaturation methods (urea and GdnHCl) Basic Protocol 4: Retrieval of stabilizing and destabilizing point mutations in a range of physiological conditions (pH: 6-9 and T: 20°C-25°C) and publication years (2010-2020) Support Protocol: Downloading the entire data of the database for academic research purposes and submission of new data in ProThermDB.

MPTherm-pred: Analysis and Prediction of Thermal Stability Changes upon Mutations in Transmembrane Proteins.

The stability of membrane proteins differs from globular proteins due to the presence of nonpolar membrane-spanning regions. Using a dataset of 929 membrane protein mutations whose effects on thermal stability (ΔTm) were experimentally determined, we found that the average ΔTm due to 190 stabilizing and 232 destabilizing mutations occurring in membrane-spanning regions are 2.43(3.1) °C and -5.48(5.5) °C, respectively. The ΔTm values for mutations occurring in solvent-exposed regions are 2.56(2.82) and - 6.8(7.2) °C. We have systematically analyzed the factors influencing the stability of mutants and observed that changes in hydrophobicity, number of contacts between Cα atoms and frequency of aliphatic residues are important determinants of the stability change induced by mutations occurring in membrane-spanning regions. We have developed structure- and sequence-based machine learning predictors of ΔTm due to mutations specifically for membrane proteins. They showed a correlation and mean absolute error (MAE) of 0.72 and 2.85 °C, respectively, between experimental and predicted ΔTm for mutations in membrane-spanning regions on 10-fold group-wise cross-validation. The average correlation and MAE for mutations in aqueous regions are 0.73 and 3.7 °C, respectively. These MAE values are about 50% lower than standard deviations from the mean ΔTm values. The reliability of the method was affirmed on a test set of mutations occurring in evolutionary independent protein sequences. The developed MPTherm-pred server for predicting thermal stability changes upon mutations in membrane proteins is available at https://web.iitm.ac.in/bioinfo2/mpthermpred/. Our results provide insights into factors influencing the stability of membrane proteins and can aid in designing mutants that are more resistant to thermal stress.

Electrochemical behavior, structural, morphological, Calf Thymus-DNA interaction and in-vitro antimicrobial studies of synthesized Schiff base transition metal complexes.

New tetradentate Schiff base transition metal complexes have been derived from salicylidene-4-imino-2,3-dimethyl-1-phenyl-3-pyrazolin-5-one and histidine were characterized by CHN analysis, magnetic susceptibility measurements, molar conductance, FAB-MS, IR, 1H-NMR, UV, CV, EPR, Fluorescence emission, AFM and Powder XRD techniques. AFM images and Powder XRD data endure that the complexes are nano-size grains with polycrystalline structure. The spectral evidences showed that all the metal chelates are square planar geometry except [VOL] complex which exist square-pyramidal geometry. Electrochemical data (CV) for [CuL] and [VOL] complexes in acetonitrile solution indicates that the redox potential of metal ions is affected by the coordinated ligand. Electron Spin Resonance (ESR) spectra of [CuL] and [VOL] complexes were well coinciding with proposed geometries and other reported complexes. CT-DNA interaction studies of [CuL] complex reveals that an intercalation binding mode occurs between complex and DNA base pairs. The in vitro antimicrobial activity of complexes has been tested against the growth of some fungal and bacterial species persist that chelates have better control than ligand.

HuVarBase: A human variant database with comprehensive information at gene and protein levels.

Human variant databases could be better exploited if the variant data available in multiple resources is integrated in a single comprehensive resource along with sequence and structural features. Such integration would improve the analyses of variants for disease prediction, prevention or treatment. The HuVarBase (HUmanVARiantdataBASE) assimilates publicly available human variant data at protein level and gene level into a comprehensive resource. Protein level data such as amino acid sequence, secondary structure of the mutant residue, domain, function, subcellular location and post-translational modification are integrated with gene level data such as gene name, chromosome number & genome position, DNA mutation, mutation type origin and rs ID number. Disease class has been added for the disease causing variants. The database is publicly available at https://www.iitm.ac.in/bioinfo/huvarbase. A total of 774,863 variant records, integrated in the HuVarBase, can be searched with options to display, visualize and download the results.

Homology modeling identified for purported drug targets to the neuroprotective effects of levodopa and asiaticoside-D in degenerated cerebral ganglions of Lumbricus terrestris.

CONTEXT: Homology modeling plays role in determining the therapeutic targets dreadful for condition such as neurodegenerative diseases (NDD), which pose challenge in achieving the effective managements. The structures of the serotonin transporter (SERT), aquaporin (AQP), and tropomyosin receptor kinase (TrkA) which are implicated in NDD pathology are still unknown for Lumbricus terrestris, but the three-dimensional (3D) structure of the human counterpart for modeling. AIM: This study aims to generate and evaluate the 3D structure of TrkA, SERT, and AQP proteins and their interaction with the ligands, namely Asiaticoside-D (AD) and levodopa (L-DOPA) the anti-NDD agents. SUBJECTS AND METHODS: Homology modeling for SERT, AQP, and TrkA proteins of Lumbricus terrestris using SWISS-MODEL Server and the modeled structure was validated using Rampage Server. Wet-lab analysis of their correspondent m-RNA levels was also done to validate the in silico data. RESULTS: It was found that TrkA had moderately high homology (67%) to human while SERT and AQP could exhibit 58% and 42%, respectively. The reliability of the model was assessed by Ramachandran plot analysis. Interactions of AD with the SERT, AQP-4, and TrkA showed the binding energies as -9.93, 8.88, and -7.58 of Kcal/mol, respectively, while for L-DOPA did show -3.93, -5.13, and -6.0 Kcal/mol, respectively. The levels of SERT, TrkA, and AQP-4 were significantly reduced (P < 0.001) on ROT induced when compared to those of control worms. On ROT + AD supplementation group (III), m-RNA levels were significantly increased (P < 0.05) when compared to those of ROT induced worms (group II). CONCLUSION: Our pioneering docking data propose the possible of target which is proved useful for therapeutic investigations against the unconquered better of NDD.

Identification and Analysis of Key Residues in Protein-RNA Complexes.

Protein-RNA complexes play important roles in various biological processes. The functions of protein-RNA complexes are dictated by their interactions, binding, stability, and affinity. In this work, we have identified the key residues (KRs), which are involved in both stability and binding. We found that 42 percent of considered proteins share common binding and stabilizing residues, whereas these residues are distinct in 58 percent of the proteins. Overall, 5 percent of stabilizing and 3 percent of binding residues serve as key residues. These residues are enriched with the combination of polar, charged, aliphatic, and aromatic residues. Analysis on subclasses of protein-RNA complexes based on protein structural class, function and RNA type showed that regulatory proteins, and complexes with single stranded RNA and rRNA have appreciable number of key residues. Specifically, Arg, Tyr, and Thr are preferred in most of the subclasses of protein-RNA complexes. In addition, residues with similar chemical behavior have different preferences to be KRs, such that Arg, Tyr, Val, and Thr are preferred over Lys, Trp, Ile, and Ser, respectively. Atomic level contacts revealed that charged and polar-nonpolar contacts are dominant in enzymes, polar in structural, and nonpolar in regulatory proteins. On the other hand, polar-nonpolar contacts are enriched in all these classes of protein-RNA complexes. Further, the influence of sequence and structural features such as conservation score, surrounding hydrophobicity, solvent accessibility, secondary structure, and long-range order in key residues are also discussed. We envisage that the present study provides insights to understand the structural and functional aspects of protein-RNA complexes.