Protein intrinsically disordered regions (IDRs) play pivotal roles in molecular recognition and regulatory processes through structural disorder-to-order transitions. To understand and exploit the distinctive functional implications of IDRs and to unravel the underlying molecular mechanisms, structural disorder-to-function relationships need to be deciphered. The DNA site-specific recombinase system Cre/loxP represents an attractive model to investigate functional molecular mechanisms of IDRs. Cre contains a functionally dispensable disordered N-terminal tail, which becomes indispensable in the evolved Tre/loxLTR recombinase system. The difficulty to experimentally obtain structural information about this tail has so far precluded any mechanistic study on its involvement in DNA recombination. Here, we use in vitro and in silico evolution data, conformational dynamics, AI-based folding simulations, thermodynamic stability calculations, mutagenesis and DNA recombination assays to investigate how evolution and the dynamic behavior of this IDR may determine distinct functional properties. Our studies suggest that partial conformational order in the N-terminal tail of Tre recombinase and its packing to a conserved hydrophobic surface on the protein provide thermodynamic stability. Based on our results, we propose a link between protein stability and function, offering new plausible atom-detailed mechanistic insights into disorder-function relationships. Our work highlights the potential of N-terminal tails to be exploited for regulation of the activity of Cre-like tyrosine-type SSRs, which merits future investigations and could be of relevance in future rational engineering for their use in biotechnology and genomic medicine.

Scoring functions are routinely deployed in structure-based drug design to quantify the potential for protein–ligand (PL) complex formation. Here, we present a new scoring function Bappl+ that is designed to predict the binding affinities of non-metallo and metallo PL complexes. Bappl+ outperforms other state-of-the-art scoring functions, achieving a high Pearson correlation coefficient of up to ~ 0.76 with low standard deviations. The biggest contributors to the increased performance are the use of a machine-learning model and the enlarged training dataset. We have also evaluated the performance of Bappl+ on target-specific proteins, which highlighted the limitations of our function and provides a way for further improvements. We believe that Bappl+ methodology could prove valuable in ranking candidate molecules against a target metallo or non-metallo protein by reliably predicting their binding affinities, thus helping in the drug discovery process.

Big data generation through sequencing of genomes and proteomes has led to over thousands of whole genomes and millions of protein sequences. However, utilization of this data to generate drug-like molecules for curing diseases remains a challenge. We propose here, Dhanvantari, a comprehensive software suite which automates the journey from genomes to hit molecules via its various modules such as i) gene finding, ii) computational structural study of target proteins and iii) virtual screening/identification of hit molecules for computer aided drug discovery. The pipeline has five possible entry points. Validation of the protocol is performed on 10 major life-threatening diseases reported by WHO covering 33 different protein targets and 111 FDA approved drugs. Three complete case studies from genomes to hits are also performed using the pipeline where the reported FDA drugs against the pathogen have been successfully recovered. The proposed software suite promises to deliver some new opportunities and insights into “genome-based” drug discovery along with the classical structure-based approaches to discover novel potential drug-like molecules. The entire protocol requires ~6–12 h. Individual steps, however, get completed within a few minutes. The pipeline can be freely accessed at http://www.scfbio-iitd.res.in/software/dhanvantari_new/Home.html.

The tyrosine-type site-specific DNA recombinase Cre recombines its target site, loxP, with high activity and specificity without cross-recombining the target sites of highly related recombinases. Understanding how Cre achieves this precision is key to be able to rationally engineer site-specific recombinases (SSRs) for genome editing applications. Previous work has revealed key residues for target site selectivity in the Cre/loxP and the related Dre/rox recombinase systems. However, enzymes in which these residues were changed to the respective counterpart only showed weak activity on the foreign target site. Here, we use molecular modeling and dynamics simulation techniques to comprehensively explore the mechanisms by which these residues determine target recognition in the context of their flanking regions in the protein-DNA interface, and we establish a structure-based rationale for the design of improved recombination activities. Our theoretical models reveal that nearest-neighbors to the specificity-determining residues are important players for enhancing SSR activity on the foreign target site. Based on the established rationale, we design new Cre variants with improved rox recombination activities, which we validate experimentally. Our work provides new insights into the target recognition mechanisms of Cre-like recombinases and represents an important step towards the rational design of SSRs for applied genome engineering.

Protein Interactions, pp. 371-398 (2020) No AccessChapter 15: An overview of protein–ligand docking and scoring algorithmsRuchika Bhat, Abhilash Jayaraj, Anjali Soni, and B. JayaramRuchika BhatDepartment of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi 110016, IndiaSupercomputing Facility for Bioinformatics and Computational Biology, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India, Abhilash JayarajSupercomputing Facility for Bioinformatics and Computational Biology, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India, Anjali SoniSupercomputing Facility for Bioinformatics and Computational Biology, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India, and B. JayaramDepartment of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi 110016, IndiaSupercomputing Facility for Bioinformatics and Computational Biology, Indian Institute of Technology, Hauz Khas, New Delhi 110016, IndiaKusuma School of Biological Sciences, Indian Institute of Technology, Hauz Khas, New Delhi 110016, Indiahttps://doi.org/10.1142/9789811211874_0015Cited by:0 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: An understanding of the rules of receptor interactions with ligand molecules is of utmost importance in the area of computer-aided drug discovery (CADD). Current docking algorithms endeavor toward prediction of biologically relevant ligands, which can bind to a specific cavity/active site of biomolecule(s)/receptor(s) inducing the required upregulation or downregulation. These algorithms aim to predict favorable orientation and conformation of a ligand (small molecule) when bound to a target receptor (protein/DNA) to make a stable complex. The effectiveness of such algorithms largely depends on the adopted mathematical model of scoring, which predicts the binding free energy between the receptor and the ligand. The quality of the available force fields and the extent of conformational sampling make binding free energy estimations challenging. However, incorporation of other approaches, such as machine learning, newer force fields and increased exploration in conformational search space, has made current generation scoring functions more promising. This chapter illustrates the broad classification of various available docking and scoring algorithms, their applications and limitations along with their comparative assessment on PDB-bind core data set of 2018 (same as 2016) release comprising 295 protein–ligand complexes. The advancements in the field of docking and scoring have led to a correlation of ~0.8 with experimental data in generic cases, whereas in specific cases a correlation of over 0.98 has also been reported. FiguresReferencesRelatedDetails Protein InteractionsMetrics History PDF download

Chikungunya has re-emerged as an epidemic with global distribution and high morbidity, necessitating the need for effective therapeutics. We utilized already approved drugs with a good safety profile used in other diseases for their new property of anti-chikungunya activity. It provides a base for a fast and efficient approach to bring a novel therapy from bench to bedside by the process of drug-repositioning. We utilized an in-silico drug screening with FDA approved molecule library to identify inhibitors of the chikungunya nsP2 protease, a multifunctional and essential non-structural protein required for virus replication. Telmisartan, an anti-hypertension drug, and the antibiotic novobiocin emerged among top hits on the screen. Further, SPR experiments revealed strong in-vitro binding of telmisartan and novobiocin to nsP2 protein. Additionally, small angle x-ray scattering suggested binding of molecules to nsP2 and post-binding compaction and retention of monomeric state in the protein-inhibitor complex. Protease activity measurement revealed that both compounds inhibited nsP2 protease activity with IC50 values in the low micromolar range. More importantly, plaque formation assays could show the effectiveness of these drugs in suppressing virus propagation in host cells. We propose novobiocin and telmisartan as potential inhibitors of chikungunya replication. Further research is required to establish the molecules as antivirals of clinical relevance against chikungunya.

Estrogen receptor (ER) has been a therapeutic target to treat ER‐positive breast cancer, most notably by agents known as selective estrogen receptor modulators (SERMs). However, resistance and severe adverse effects of known drugs gave impetus to the search for newer agents with better therapeutic profile. ERα and ERβ are two isoforms sharing 56% identity and having different physiological functions and expressions in various tissues. Only two residues differ in the active sites of the two isoforms motivating us to design isoform‐selective ligands. Guided by computational docking and molecular dynamics simulations, we have designed, synthesized, and tested, substituted biphenyl‐2,6‐diethanones and their derivatives as potential agents targeting ERα. Four of the molecules synthesized exhibited preferential cytotoxicity in ERα+ cell line (MCF‐7) compared to ERβ+ cell line (MDA‐MB‐231). Molecular dynamics (MD) in combination with molecular mechanics‐generalized Born surface area (MM‐GBSA) methods could account for binding selectivity. Further cotreatment and E‐screen studies with known ER ligands—estradiol (E2) and tamoxifen (Tam)—indicated isoform‐selective anti‐estrogenicity in ERα+ cell line which might be ER‐mediated. ERα siRNA silencing experiments further confirmed the ER selective nature of ligands.

Motivation: Drug intercalation is an important strategy for DNA inhibition which is often employed in cancer chemotherapy. Despite its high significance, the field is characterized by limited success in identification of novel intercalator molecules and lack of automated and dedicated drug-DNA intercalation methodology.Results: We report here a novel intercalation methodology (christened 'Intercalate') for predicting both the structures and energetics of DNA-intercalator complexes, covering the processes of DNA unwinding and (non-covalent) binding. Given a DNA sequence and intercalation site information, Intercalate generates the 3D structure of DNA, creates the intercalation site, performs docking at the intercalation site and evaluates DNA-intercalator binding energy in an automated way. The structures and energetics of the DNA-intercalator complexes produced by Intercalate methodology are seen to be in good agreement with experiment. The dedicated attempt made in developing a drug-DNA intercalation methodology (compatible with its mechanism) with high accuracy should prove useful in the discovery of potential intercalators for their use as anticancers, antibacterials or antivirals.Supplementary information: Supplementary data are available at Bioinformatics online.

The growing genomic and proteomic sequence/structural databases trigger high expectations for a rapid and successful treatment of diseases and disorders. In the current biological information, rich and functional knowledge, poor scenario, the feasibility of creating an automated genomes to hits (G2H) assembly line in order to cut down the cost and time in drug discovery is discussed. The G2H computational pathway involves several challenging research areas viz. functional annotation of genomes, identification of druggable targets, prediction of three-dimensional structures of protein targets from their amino acid sequences, and hit molecule generation for these targets followed by a transition from bench to bedside. We describe the ‘G2H In Silico’ strategy (called Dhanvantari), and illustrate it on Chikungunya virus (CHIKV). G2H is a novel pathway incorporating, a series of steps such as gene prediction (Chemgenome), protein tertiary structure determination (Bhageerath), automated active site identification, rapid hit molecule generation followed by atomic level docking and scoring of hits to arrive at lead compounds (Sanjeevini). The current state of the art for each of the steps in the pathway will be highlighted and the results will be presented and discussed.

These are exciting times for bioinformaticians, computational biologists and drug designers with the genome and proteome sequences and related structural databases growing at an accelerated pace. The post-genomic era has triggered high expectations for a rapid and successful treatment of diseases. However, in this biological information rich and functional knowledge poor scenario, the challenges are indeed grand, no less than the assembly of the genome of the whole organism. These include functional annotation of genes, identification of druggable targets, prediction of three-dimensional structures of protein targets from their amino acid sequences, arriving at lead compounds for these targets followed by a transition from bench to bedside. We propose here a "Genome to Hits In Silico" strategy (called Dhanvantari) and illustrate it on Chikungunya virus (CHIKV). "Genome to hits" is a novel pathway incorporating a series of steps such as gene prediction, protein tertiary structure determination, active site identification, hit molecule generation, docking and scoring of hits to arrive at lead compounds. The current state of the art for each of the steps in the pathway is high-lighted and the feasibility of creating an automated genome to hits assembly line is discussed.

The discovery of new pharmaceuticals via computer modeling is one of the key challenges in modern medicine. The advent of global networks of genomic, proteomic and metabolomic endeavors is ushering in an increasing number of novel and clinically important targets for screening. Computational methods are anticipated to play a pivotal role in exploiting the structural and functional information to understand specific molecular recognition events of the target macromolecule with candidate hits leading ultimately to the design of improved leads for the target. In this review, we sketch a system independent, comprehensive physicochemical pathway for lead molecule design focusing on the emerging in silico trends and techniques. We survey strategies for the generation of candidate molecules, docking them with the target and ranking them based on binding affinities. We present a molecular level treatment for distinguishing affinity from specificity of a ligand for a given target. We also discuss some significant aspects of drug absorption, distribution, metabolism, excretion and toxicity (ADMET) and highlight improved protocols required for higher quality and throughput of in silico methods employed at early stages of discovery. We present a realization of the various stages in the pathway proposed with select examples from the literature and from our own research to demonstrate the way in which an iterative process of computer design and validation can aid in developing potent leads. The review thus summarizes recent advances and presents a viewpoint on improvements envisioned in the years to come for automated computer aided lead molecule discovery.