Homology modeling a fast tool for drug discovery: current perspectives

Homology Modeling of 5-alpha-Reductase 2 Using Available Experimental Data

5-Alpha-reductase 2 is an interesting pharmaceutical target for the treatment of several diseases, including prostate cancer, benign prostatic hyperplasia, male pattern baldness, acne, and hirsutism. One of the main approaches in computer aided drug design is structure-based drug discovery. However, the experimental 3D structure of 5-alpha-reductase 2 is not available at present. Therefore, a homology modeling method and molecular dynamics simulation were used to develop a reliable model of 5-alpha-reductase 2 for inhibitor pose prediction and virtual screening. Despite the low sequence identity between the target and template sequences, a useful 3D model of 5-alpha-reductase 2 was generated by the inclusion of experimental data.

Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform

BACKGROUND

Traditional drug discovery approaches focus on a limited set of target molecules for treatment against specific indications/diseases. However, drug absorption, dispersion, metabolism, and excretion (ADME) involve interactions with multiple protein systems. Drugs approved for particular indication(s) may be repurposed as novel therapeutics for others. The severely declining rate of discovery and increasing costs of new drugs illustrate the limitations of the traditional reductionist paradigm in drug discovery.

METHODS

We developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform based on a hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for therapeutic repurposing and discovery. We compiled a library of compounds that are human ingestible with minimal side effects, followed by an 'all-compounds' vs 'all-proteins' fragment-based multitarget docking with dynamics screen to construct compound-proteome interaction matrices that were then analyzed to determine similarity of drug behavior. The proteomic signature similarity of drugs is then ranked to make putative drug predictions for all indications in a shotgun manner.

RESULTS

We have previously applied this platform with success in both retrospective benchmarking and prospective validation, and to understand the effect of druggable protein classes on repurposing accuracy. Here we use the CANDO platform to analyze and determine the contribution of multitargeting (polypharmacology) to drug repurposing benchmarking accuracy. Taken together with the previous work, our results indicate that a large number of protein structures with diverse fold space and a specific polypharmacological interactome is necessary for accurate drug predictions using our proteomic and evolutionary drug discovery and repurposing platform.

CONCLUSION

These results have implications for future drug development and repurposing in the context of polypharmacology.

Improvising 5-HT7R homology model for design of high affinity ligands: model validation with docking, embrace minimization, MM-GBSA, and molecular dynamic simulations

The subtype, 5-HT₇R has been implicated in neurological disorders and presents itself as a promising target for antidepressant drugs. Design of targeted selective ligands, require a sound knowledge of 3D-receptor structure. In absence of receptor structure, structure-based design of targeted ligands relies on generation of 5-HT₇R homology model. In this study, the impact of template choice, alignment, and model building methods on the homology model of 5-HT₇R is addressed. The compactness and model quality due to the presence of cholesterol (lipidic receptor) have also been observed. The results suggest good stereochemical quality of the final model. Ramachandran Plot Analysis indicated more than 97.5% amino acid residues in the favorable region. The overall quality factor was 91.8% using ERRAT. The Z-score for backbone confirmation and packing quality were -1.248 and -1.427, respectively, using WHATCHECK. The RMS Z-score for side chain planarity was .711. Other validation results for the final model include binding site analysis in which Asp162, Val163, Phe343, Phe344, Arg350, Arg367, and Leu370 conserved residues were found in the active site, correlation coefficient of .82 in ligand-based screening and .85 in embrace minimization. Further, the model showed good correlation for agonist and antagonist in docking ([Formula: see text] ≈ .76, [Formula: see text] ≈ .82), embrace minimization ([Formula: see text] ≈ .73, [Formula: see text] ≈ .72), and MM-GBSA ([Formula: see text] ≈ .69, [Formula: see text] ≈ .75) studies. The model was subjected to Molecular Dynamics (MD) simulation of 20 ns both in ligand-free and ligand-bound receptor (agonist and antagonist) system in order to assess its stability.

Molecular characterization of EcCIPK24 gene of finger millet (Eleusine coracana) for investigating its regulatory role in calcium transport

Finger millet grains contain exceptionally high levels of calcium which is much higher compared to other cereals and millets. Since calcium is an important macronutrient in human diet, it is necessary to explore the molecular basis of calcium accumulation in the seeds of finger millet. CIPK is a calcium sensor gene, having role in activating Ca²⁺ exchanger protein by interaction with CBL proteins. To know the role of EcCIPK24 gene in seed Ca²⁺ accumulation, sequence is retrieved from the transcriptome data of two finger millet genotypes GP1 (low Ca²⁺) and GP45 (high Ca²⁺), and the expression was determined through qRT-PCR. The higher expression was found in root, shoot, leaf and developing spike tissue of GP45 compared to GP1; structural analysis showed difference of nine SNPs and one extra beta sheet domain as well as differences in vacuolar localization was predicted; besides, the variation in amino acid composition among both the genotypes was also investigated. Molecular modeling and docking studies revealed that both EcCBL4 and EcCBL10 showed strong binding affinity with EcCIPK24 (GP1) compared to EcCIPK24 (GP45). It indicates a genotypic structural variation, which not only affects the affinity but also calcium transport efficiency after interaction of CIPK-CBL with calcium exchanger (EcCAX1b) to pull calcium in the vacuole. Based on the expression and in silico study, it can be suggested that by activating EcCAX1b protein, EcCIPK24 plays an important role in high seed Ca²⁺ accumulation.

Recent Advances in Structure-Based Drug Design Targeting Class A G Protein-Coupled Receptors Utilizing Crystal Structures and Computational Simulations

G protein-coupled receptors (GPCRs) represent the largest and most physiologically important integral membrane protein family, and these receptors respond to a wide variety of physiological and environmental stimuli. GPCRs are among the most critical therapeutic targets for numerous human diseases, and approximately one-third of the currently marketed drugs target this receptor family. The recent breakthroughs in GPCR structural biology have significantly contributed to our understanding of GPCR function, ligand binding, and pharmacological action as well as to the design of new drugs. This perspective highlights the latest advances in GPCR structures with a focus on the receptor-ligand interactions of each receptor family in class A nonrhodopsin GPCRs as well as the structural features for their activation, biased signaling, and allosteric mechanisms. The current state-of-the-art methodologies of structure-based drug design (SBDD) approaches in the GPCR research field are also discussed.

Serum Albumin Binding and Esterase Activity: Mechanistic Interactions with Organophosphates

The albumin molecule, in contrast to many other plasma proteins, is not covered with a carbohydrate moiety and can bind and transport various molecules of endogenous and exogenous origin. The enzymatic activity of albumin, the existence of which many scientists perceive skeptically, is much less studied. In toxicology, understanding the mechanistic interactions of organophosphates with albumin is a special problem, and its solution could help in the development of new types of antidotes. In the present work, the history of the issue is briefly examined, then our in silico data on the interaction of human serum albumin with soman, as well as comparative in silico data of human and bovine serum albumin activities in relation to paraoxon, are presented. Information is given on the substrate specificity of albumin and we consider the possibility of its affiliation to certain classes in the nomenclature of enzymes.

Design strategies of oxidosqualene cyclase inhibitors: Targeting the sterol biosynthetic pathway

Targeting the sterol biosynthesis pathway has been explored for the development of new bioactive compounds. Among the enzymes of this pathway, oxidosqualene cyclase (OSC) which catalyzes lanosterol cyclization from 2,3-oxidosqualene has emerged as an attractive target. In this work, we reviewed the most promising OSC inhibitors from different organisms and their potential for the development of new antiparasitic, antifungal, hypocholesterolemic and anticancer drugs. Different strategies have been adopted for the discovery of new OSC inhibitors, such as structural modifications of the natural substrate or the reaction intermediates, the use of the enzyme's structural information to discover compounds with novel chemotypes, modifications of known inhibitors and the use of molecular modeling techniques such as docking and virtual screening to search for new inhibitors. This review brings new perspectives on structural insights of OSC from different organisms and reveals the broad structural diversity of OSC inhibitors which may help evidence lead compounds for further investigations with various therapeutic applications.

Theoretical analysis of somatostatin receptor 5 with antagonists and agonists for the treatment of neuroendocrine tumors

We report on SSTR5 receptor modeling and its interaction with reported antagonist and agonist molecules. Modeling of the SSTR5 receptor was carried out using multiple templates with the aim of improving the precision of the generated models. The selective SSTR5 antagonists, agonists and native somatostatin SRIF-14 were employed to propose the binding site of SSTR5 and to identify the critical residues involved in the interaction of the receptor with other molecules. Residues Q2.63, D3.32, Q3.36, C186, Y7.34 and Y7.42 were found to be highly significant for their strong interaction with the receptor. SSTR5 antagonists were utilized to perform a 3D quantitative structure-activity relationship study. A comparative molecular field analysis (CoMFA) was conducted using two different alignment schemes, namely the ligand-based and receptor-based alignment methods. The best statistical results were obtained for ligand-based ([Formula: see text], [Formula: see text] = 0.988, noc = 4) and receptor-guided methods (docked mode 1:[Formula: see text], [Formula: see text], noc = 5), (docked mode 2:[Formula: see text] = 0.555, [Formula: see text], noc = 5). Based on CoMFA contour maps, an electropositive substitution at [Formula: see text], [Formula: see text] and [Formula: see text] position and bulky group at [Formula: see text] position are important in enhancing molecular activity.

The History and Development of Quantitative Structure-Activity Relationships (QSARs)

It is widely accepted that modern QSAR began in the early 1960s. However, as long ago as 1816 scientists were making predictions about physical and chemical properties. The first investigations into the correlation of biological activities with physicochemical properties such as molecular weight and aqueous solubility began in 1841, almost 60 years before the important work of Overton and Meyer linking aquatic toxicity to lipid-water partitioning. Throughout the 20th century QSAR progressed, though there were many lean years. In 1962 came the seminal work of Corwin Hansch and co-workers, which stimulated a huge interest in the prediction of biological activities. Initially that interest lay largely within medicinal chemistry and drug design, but in the 1970s and 1980s, with increasing ecotoxicological concerns, QSAR modelling of environmental toxicities began to grow, especially once regulatory authorities became involved. Since then QSAR has continued to expand, with over 1400 publications annually from 2011 onwards.

Computer-Aided Antibody Design: An Overview

The use of monoclonal antibody as the next generation protein therapeutics with remarkable success has surged the development of antibody engineering to design molecules for optimizing affinity, better efficacy, greater safety and therapeutic function. Therefore, computational methods have become increasingly important to generate hypotheses, interpret and guide experimental works. In this chapter, we discussed the overall antibody design by computational approches.

Computational methods in drug discovery

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Computational Approaches for the Discovery of Human Proteasome Inhibitors: An Overview

Proteasome emerged as an important target in recent pharmacological research due to its pivotal role in degrading proteins in the cytoplasm and nucleus of eukaryotic cells, regulating a wide variety of cellular pathways, including cell growth and proliferation, apoptosis, DNA repair, transcription, immune response, and signaling processes. The last two decades witnessed intensive efforts to discover 20S proteasome inhibitors with significant chemical diversity and efficacy. To date, the US FDA approved to market three proteasome inhibitors: bortezomib, carfilzomib, and ixazomib. However new, safer and more efficient drugs are still required. Computer-aided drug discovery has long being used in drug discovery campaigns targeting the human proteasome. The aim of this review is to illustrate selected in silico methods like homology modeling, molecular docking, pharmacophore modeling, virtual screening, and combined methods that have been used in proteasome inhibitors discovery. Applications of these methods to proteasome inhibitors discovery will also be presented and discussed to raise improvements in this particular field.

Computational modeling of Repeat1 region of INI1/hSNF5: An evolutionary link with ubiquitin

The structure of a protein can be very informative of its function. However, determining protein structures experimentally can often be very challenging. Computational methods have been used successfully in modeling structures with sufficient accuracy. Here we have used computational tools to predict the structure of an evolutionarily conserved and functionally significant domain of Integrase interactor (INI)1/hSNF5 protein. INI1 is a component of the chromatin remodeling SWI/SNF complex, a tumor suppressor and is involved in many protein-protein interactions. It belongs to SNF5 family of proteins that contain two conserved repeat (Rpt) domains. Rpt1 domain of INI1 binds to HIV-1 Integrase, and acts as a dominant negative mutant to inhibit viral replication. Rpt1 domain also interacts with oncogene c-MYC and modulates its transcriptional activity. We carried out an ab initio modeling of a segment of INI1 protein containing the Rpt1 domain. The structural model suggested the presence of a compact and well defined ββαα topology as core structure in the Rpt1 domain of INI1. This topology in Rpt1 was similar to PFU domain of Phospholipase A2 Activating Protein, PLAA. Interestingly, PFU domain shares similarity with Ubiquitin and has ubiquitin binding activity. Because of the structural similarity between Rpt1 domain of INI1 and PFU domain of PLAA, we propose that Rpt1 domain of INI1 may participate in ubiquitin recognition or binding with ubiquitin or ubiquitin related proteins. This modeling study may shed light on the mode of interactions of Rpt1 domain of INI1 and is likely to facilitate future functional studies of INI1.

Building a virtual ligand screening pipeline using free software: a survey

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems.

Binding Free Energy Calculations of Nine FDA-approved Protease Inhibitors Against HIV-1 Subtype C I36T↑T Containing 100 Amino Acids Per Monomer

In this work, have investigated the binding affinities of nine FDA-approved protease inhibitor drugs against a new HIV-1 subtype C mutated protease, I36T↑T. Without an X-ray crystal structure, homology modelling was used to generate a three-dimensional model of the protease. This and the inhibitor models were employed to generate the inhibitor/I36T↑T complexes, with the relative positions of the inhibitors being superimposed and aligned using the X-ray crystal structures of the inhibitors/HIV-1 subtype B complexes as a reference. Molecular dynamics simulations were carried out on the complexes to calculate the average binding free energies for each inhibitor using the molecular mechanics generalized Born surface area (MM-GBSA) method. When compared to the binding free energies of the HIV-1 subtype B and subtype C proteases (calculated previously by our group using the same method), it was clear that the I36T↑T proteases mutations and insertion had a significant negative effect on the binding energies of the non-pepditic inhibitors nelfinavir, darunavir and tipranavir. On the other hand, ritonavir, amprenavir and indinavir show improved calculated binding energies in comparison with the corresponding data for wild-type C-SA protease. The computational model used in this study can be used to investigate new mutations of the HIV protease and help in establishing effective HIV drug regimes and may also aid in future protease drug design.

Insight into the selective inhibition of JNK family members through structure-based drug design

The c-Jun N-terminal kinase (JNK) family, which comprises JNK1, JNK2 and JNK3, belongs to the mitogen-activated protein kinase (MAPK) superfamily, whose members regulate myriad biological processes, including those implicated in tumorigenesis and neurodegenerative disorders.

A computational chemistry perspective on the current status and future direction of hepatitis B antiviral drug discovery

Computational chemical biology, applied to research on hepatitis B virus (HBV), has two major branches: bioinformatics (statistical models) and first-principle methods (molecular physics). While bioinformatics focuses on statistical tools and biological databases, molecular physics uses mathematics and chemical theory to study the interactions of biomolecules. Three computational techniques most commonly used in HBV research are homology modeling, molecular docking, and molecular dynamics. Homology modeling is a computational simulation to predict protein structure and has been used to construct conformers of the viral polymerase (reverse transcriptase domain and RNase H domain) and the HBV X protein. Molecular docking is used to predict the most likely orientation of a ligand when it is bound to a protein, as well as determining an energy score of the docked conformation. Molecular dynamics is a simulation that analyzes biomolecule motions and determines conformation and stability patterns. All of these modeling techniques have aided in the understanding of resistance mutations on HBV non-nucleos(t)ide reverse-transcriptase inhibitor binding. Finally, bioinformatics can be used to study the DNA and RNA protein sequences of viruses to both analyze drug resistance and to genotype the viral genomes. Overall, with these techniques, and others, computational chemical biology is becoming more and more necessary in hepatitis B research. This article forms part of a symposium in Antiviral Research on "An unfinished story: from the discovery of the Australia antigen to the development of new curative therapies for hepatitis B."

A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets

Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for “growing” the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.

Genetically modified proteins: functional improvement and chimeragenesis

This review focuses on the emerging role of site-specific mutagenesis and chimeragenesis for the functional improvement of proteins in areas where traditional protein engineering methods have been extensively used and practically exhausted. The novel path for the creation of the novel proteins has been created on the farther development of the new structure and sequence optimization algorithms for generating and designing the accurate structure models in result of x-ray crystallography studies of a lot of proteins and their mutant forms. Artificial genetic modifications aim to expand nature's repertoire of biomolecules. One of the most exciting potential results of mutagenesis or chimeragenesis finding could be design of effective diagnostics, bio-therapeutics and biocatalysts. A sampling of recent examples is listed below for the in vivo and in vitro genetically improvement of various binding protein and enzyme functions, with references for more in-depth study provided for the reader's benefit.

Structure Based Modeling of Small Molecules Binding to the TLR7 by Atomistic Level Simulations

Toll-Like Receptors (TLR) are a large family of proteins involved in the immune system response. Both the activation and the inhibition of these receptors can have positive effects on several diseases, including viral pathologies and cancer, therefore prompting the development of new compounds. In order to provide new indications for the design of Toll-Like Receptor 7 (TLR7)-targeting drugs, the mechanism of interaction between the TLR7 and two important classes of agonists (imidazoquinoline and adenine derivatives) was investigated through docking and Molecular Dynamics simulations. To perform the computational analysis, a new model for the dimeric form of the receptors was necessary and therefore created. Qualitative and quantitative differences between agonists and inactive compounds were determined. The in silico results were compared with previous experimental observations and employed to define the ligand binding mechanism of TLR7.

α-Thalassemia associated with hb instability: a tale of two features. the case of Hb Rogliano or α1 Cod 108(G15)Thr→Asn and Hb Policoro or α2 Cod 124(H7)Ser→Pro

We identified two new variants in the third exon of the α-globin gene in families from southern Italy: the Hb Rogliano, α1 cod108 ACC>AAC or α1[α108(G15)Thr→Asn] and the Hb Policoro, α2 cod124 TCC>CCC or α2[α124(H7)Ser→Pro]. The carriers showed mild α-thalassemia phenotype and abnormal hemoglobin stability features. These mutations occurred in the G and H helices of the α-globin both involved in the specific recognition of AHSP and β1 chain. Molecular characterization of mRNA, globin chain analyses and molecular modelling studies were carried out to highlight the mechanisms causing the α-thalassemia phenotype. The results demonstrated that the α-thalassemia defect associated with the two Hb variants originated by different defects. Hb Rogliano showed an intrinsic instability of the tetramer due to anomalous intra- and inter-chain interactions suggesting that the variant chain is normally synthesized and complexed with AHSP but rapidly degraded because it is unable to form the α1β1 dimers. On the contrary in the case of Hb Policoro two different molecular mechanisms were shown: the reduction of the variant mRNA level by an unclear mechanism and the protein instability due to impairment of AHSP interaction. These data highlighted that multiple approaches, including mRNA quantification, are needed to properly identify the mechanisms leading to the α-thalassemia defect. Elucidation of the specific mechanism leads to the definition of a given phenotype providing important guidance for the diagnosis of unstable variants.

Strategies and molecular tools to fight antimicrobial resistance: resistome, transcriptome, and antimicrobial peptides

The increasing number of antibiotic resistant bacteria motivates prospective research toward discovery of new antimicrobial active substances. There are, however, controversies concerning the cost-effectiveness of such research with regards to the description of new substances with novel cellular interactions, or description of new uses of existing substances to overcome resistance. Although examination of bacteria isolated from remote locations with limited exposure to humans has revealed an absence of antibiotic resistance genes, it is accepted that these genes were both abundant and diverse in ancient living organisms, as detected in DNA recovered from Pleistocene deposits (30,000 years ago). Indeed, even before the first clinical use of antibiotics more than 60 years ago, resistant organisms had been isolated. Bacteria can exhibit different strategies for resistance against antibiotics. New genetic information may lead to the modification of protein structure affecting the antibiotic carriage into the cell, enzymatic inactivation of drugs, or even modification of cellular structure interfering in the drug-bacteria interaction. There are still plenty of new genes out there in the environment that can be appropriated by putative pathogenic bacteria to resist antimicrobial agents. On the other hand, there are several natural compounds with antibiotic activity that may be used to oppose them. Antimicrobial peptides (AMPs) are molecules which are wide-spread in all forms of life, from multi-cellular organisms to bacterial cells used to interfere with microbial growth. Several AMPs have been shown to be effective against multi-drug resistant bacteria and have low propensity to resistance development, probably due to their unique mode of action, different from well-known antimicrobial drugs. These substances may interact in different ways with bacterial cell membrane, protein synthesis, protein modulation, and protein folding. The analysis of bacterial transcriptome may contribute to the understanding of microbial strategies under different environmental stresses and allows the understanding of their interaction with novel AMPs.

Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: implications for three-dimensional modeling

With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (∼85%) and specific (∼95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions.

A novel antibody humanization method based on epitopes scanning and molecular dynamics simulation

1-17-2 is a rat anti-human DEC-205 monoclonal antibody that induces internalization and delivers antigen to dendritic cells (DCs). The potentially clinical application of this antibody is limited by its murine origin. Traditional humanization method such as complementarity determining regions (CDRs) graft often leads to a decreased or even lost affinity. Here we have developed a novel antibody humanization method based on computer modeling and bioinformatics analysis. First, we used homology modeling technology to build the precise model of Fab. A novel epitope scanning algorithm was designed to identify antigenic residues in the framework regions (FRs) that need to be mutated to human counterpart in the humanization process. Then virtual mutation and molecular dynamics (MD) simulation were used to assess the conformational impact imposed by all the mutations. By comparing the root-mean-square deviations (RMSDs) of CDRs, we found five key residues whose mutations would destroy the original conformation of CDRs. These residues need to be back-mutated to rescue the antibody binding affinity. Finally we constructed the antibodies in vitro and compared their binding affinity by flow cytometry and surface plasmon resonance (SPR) assay. The binding affinity of the refined humanized antibody was similar to that of the original rat antibody. Our results have established a novel method based on epitopes scanning and MD simulation for antibody humanization.

Personalized prediction of EGFR mutation-induced drug resistance in lung cancer

EGFR mutation-induced drug resistance has significantly impaired the potency of small molecule tyrosine kinase inhibitors in lung cancer treatment. Computational approaches can provide powerful and efficient techniques in the investigation of drug resistance. In our work, the EGFR mutation feature is characterized by the energy components of binding free energy (concerning the mutant-inhibitor complex), and we combine it with specific personal features for 168 clinical subjects to construct a personalized drug resistance prediction model. The 3D structure of an EGFR mutant is computationally predicted from its protein sequence, after which the dynamics of the bound mutant-inhibitor complex is simulated via AMBER and the binding free energy of the complex is calculated based on the dynamics. The utilization of extreme learning machines and leave-one-out cross-validation promises a successful identification of resistant subjects with high accuracy. Overall, our study demonstrates advantages in the development of personalized medicine/therapy design and innovative drug discovery.

The eradication of leprosy: molecular modeling techniques for novel drug discovery

INTRODUCTION

Leprosy is a slowly progressing bacterial infection caused by Mycobacterium leprae. The World Health Organization recommended multidrug therapy (MDT) which is extremely effective and halts the progress of the disease. Even though the objective of eliminating leprosy as a public health problem has been achieved successfully, leprosy is not yet eradicated. Furthermore, the long-term use of MDT results in single- and multidrug resistance. Therefore, there is still a need for new drug discovery for leprosy.

AREAS COVERED

The authors explain the importance of discovery of new drug to leprosy and the significance of homology modeling to drug discovery. This review highlights the principle steps, applications, and the resources of homology modeling. Finally, the authors emphasize the application of different structure-based drug design (SBDD) approaches to design novel therapeutics for leprosy.

EXPERT OPINION

MDT has proved to be effective in controlling infection, with prevalence of leprosy now predominantly isolated to the developing countries. The emergence of single- and multidrug-resistant strains of M. leprae has, however, provided some concern with the need for newer antibacterial agents. Drug resistance can be overcome by multi-targeted therapy. SBDD approaches, which reported many successful drugs, depend predominantly on the three-dimensional (3D) structure of drug targets. As of 2013, only very few experimental structures are available for M. leprae proteins. Hence, SBDD, in leprosy research, relies heavily on homology modeling to predict the 3D structure of drug targets and to design better therapeutics.

Modelling and enhanced molecular dynamics to steer structure-based drug discovery

The ever-increasing gap between the availabilities of the genome sequences and the crystal structures of proteins remains one of the significant challenges to the modern drug discovery efforts. The knowledge of structure-dynamics-functionalities of proteins is important in order to understand several key aspects of structure-based drug discovery, such as drug-protein interactions, drug binding and unbinding mechanisms and protein-protein interactions. This review presents a brief overview on the different state of the art computational approaches that are applied for protein structure modelling and molecular dynamics simulations of biological systems. We give an essence of how different enhanced sampling molecular dynamics approaches, together with regular molecular dynamics methods, assist in steering the structure based drug discovery processes.