All are not equal: a benchmark of different homology modeling programs

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

HTLV-1 reverse transcriptase homology model provides structural basis for sensitivity to existing nucleoside/nucleotide reverse transcriptase inhibitors

The human T-lymphotropic virus type 1 (HTLV-1) infects millions of people globally and is endemic to various resource-limited regions. Infections persist for life and are associated with increased susceptibility to opportunistic infections and severe diseases including adult T cell leukemia/lymphoma and HTLV-1-associated myelopathy-tropical spastic paraparesis. No HTLV-1-specific anti-retrovirals have been developed and it is unclear whether existing anti-retrovirals developed for treatment of human immunodeficiency virus (HIV) have efficacy against HTLV-1. To understand the structural basis for therapeutic binding, homology modelling and machine learning were used to develop a structural model of the HTLV-1 reverse transcriptase. With this, molecular docking experiments using a panel of FDA-approved inhibitors of viral reverse transcriptases to assess their capacity for binding, and in turn, inhibition. Importantly, nucleoside/nucleotide reverse transcriptase inhibitor but not non-nucleoside reverse transcriptase inhibitors were predicted to bind the HTLV-1 reverse transcriptase, with similar affinity to HIV-1 reverse transcriptase. By strengthening the rationale for clinical testing of therapies such as tenofovir alafenamide, zidovudine, lamivudine, and azvudine for treatment of HTLV-1, this study has demonstrated the power of in silico structural biology approaches in drug design and therapeutic testing.

Comparative homology of Pleurotus ostreatus laccase enzyme: Swiss model or Modeller?

Laccases stand out in the industrial context due to their versatile biotechnological applications. Although these enzymes are frequently investigated, currently, Pleurotus ostreatus laccase structural model is unknown. Therefore, this research aims to predict and validate a P. ostreatus laccase theoretical model by means of comparative homology. The laccase target's primary structure (AOM73725.1) was obtained from the NCBI database, the model was predicted from homologous structures obtained from the PDB (PDB-ID: 5A7E, 2HRG, 4JHU, 1GYC) using the Swiss-Model and Modeller, and was refined in GalaxyRefine. The models were validated using PROCHECK, VERIFY 3D, ERRAT, PROVE and QMEAN Z-score servers. Moreover, molecular docking between the laccase model (Lacc4MN) and ABTS was performed on AutoDock Vina. The models that were generated by the Modeller showed superior stereochemical and structural characteristics to those predicted by the Swiss Model. The refinement made it difficult to stabilize the copper atoms which are typical of laccases. The Lacc4MN model showed the interactions between the amino acids in the active site of the laccase and the copper atoms, thereby hinting the stabilization of the metal through electrostatic interactions with histidine and cysteine. The molecular docking between Lacc4MN and ABTS showed negative free energy and the formation of two hydrogen bonds involving the amino acids ASP 208 and GLY 268, and a Pi-sulfur bond between residue HIS 458 and ABTS, which demonstrates the typical catalytic functionality of laccases. Furthermore, the theoretical model Lacc4MN presented stereochemical and structural characteristics that allow its use in silico tests.Communicated by Ramaswamy H. Sarma.

A new approach used in docking study for predicting the combination drug efficacy in EML4-ALK target of NSCLC

Combination drug treatments are usually used in many diseases, including cancers and AIDS. This treatment strategy is known as one of the cornerstone in therapies, which potentially reduces drug toxicity and drug resistance and also enhances therapeutic efficacy. Before using a drug in treatment, several experimental studies are done in vivo and in vitro to ensure the drug's efficacy. In such experimental studies, the drug's efficacy is evaluated with the help of drug dose ratio. In the combination drug experimental studies, the efficacy of the drugs is quantified with the Combination Index (CI) value and then interpreted by various terminologies like synergy, additive, and antagonism. Several computational models have now been invented for the speedy identification of combination drug efficacy. Unfortunately, none of these models have predicted the atomic level interaction of the combination drug with the target protein. This type of intermolecular interaction can be identified with the help of docking software. In the proposed work, we try to identify the intermolecular interaction and efficacy of the combination drug Crzizotinib and Temozolomide in the target of EML4-ALK in NSCLC by in silico study. The result of the study was evaluated with drug properties and Complex Energy (CE) of the docked complex rather than using docking score and binding energy. From this study, we could understand that first, Crizotinib and then after the Temozolomide drug binded on the EML4-ALK protein complex, showed very least CE and also identified that the combination of Crizotinib and Temozolomide drug are more effective in NSCLC.Communicated by Ramaswamy H. Sarma.

A Molecular Docking Study of Human STEAP2 for the Discovery of New Potential Anti-Prostate Cancer Chemotherapeutic Candidates

Prostate cancer is a rising health concern and accounts for 3.8% of all cancer deaths globally. Uganda has one of the highest incidence rates of the disease in Africa at 5.2% with the majority of diagnosed patients found to have advanced disease. This study aimed to use the STEAP2 protein (prostate cancer–specific biomarker) for the discovery of new targeted therapy. To determine the most likely compound that can bind to the STEAP2 protein, we docked the modeled STEAP2 3D structure against 2466 FDA (Food and Drug Administration)-approved drug candidates using AutoDock Vina. Protein basic local alignment search tool (BLASTp) search, multiple sequence alignment (MSA), and phylogenetics were further carried out to analyze the diversity of this marker and determine its conserved domains as suitable target regions. Six promising drug candidates (ligands) were identified. Triptorelin had the highest binding energy (−12.1 kcal/mol) followed by leuprolide (docking energy: −11.2 kcal/mol). All the top two drug candidates interacted with residues Ser-372 and Gly-369 in close proximity with the iron-binding domain (an important catalyst of metal reduction). The two drugs had earlier been approved for the treatment of advanced prostate cancer with an elusive mode of action. Through this study, further insight into figuring out their interaction with STEAP2 might be important during treatment.

Structure and dynamics of the somatostatin receptor 3-ligand binding in the presence of lipids examined using computational structural biology methods

In the past two decades, the structural biology studies on G-protein coupled receptors (GPCRs) are on the rise. Understanding the relation between the structure and function of GPCRs is important as they play a huge role in various signaling mechanisms in a eukaryotic cell. Somatostatin receptor 3 (SSTR3), one of the GPCRs, is one such important receptor which oversees different cellular processes including cell-to-cell signaling. However, the information available regarding the structural features of SSTR3 responsible for their bioactivity is scarce. In this study, we report a structural understanding of SSTR3-ligand binding that could be helpful in demystifying the structural complexities related to functioning of the receptor. An integrated protocol consisting of different computational structural biology tools including protein structure prediction via comparative modeling, binding site characterization, three-dimensional quantitative structure-activity relationship based on comparative molecular field analysis and comparative molecular similarity indices analysis, density functional theory, and molecular dynamics simulations were performed. Different understandings from the simulation of SSTR3-ligand complexes, mainly the conditions that are favorable for the formation of lowest bioactive state of SSTR3 ligands are reported. In addition to that, we report the important physicochemical descriptors of SSTR3 ligands that could significantly influence their bioactivity. The results of the study could be helpful in developing novel SSTR3 ligands (both agonists and antagonists) with high potency and receptor selectivity.

Virtual screening of potential anticancer drugs based on microbial products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

From Genome to Drugs: New Approaches in Antimicrobial Discovery

Decades of successful use of antibiotics is currently challenged by the emergence of increasingly resistant bacterial strains. Novel drugs are urgently required but, in a scenario where private investment in the development of new antimicrobials is declining, efforts to combat drug-resistant infections become a worldwide public health problem. Reasons behind unsuccessful new antimicrobial development projects range from inadequate selection of the molecular targets to a lack of innovation. In this context, increasingly available omics data for multiple pathogens has created new drug discovery and development opportunities to fight infectious diseases. Identification of an appropriate molecular target is currently accepted as a critical step of the drug discovery process. Here, we review how diverse layers of multi-omics data in conjunction with structural/functional analysis and systems biology can be used to prioritize the best candidate proteins. Once the target is selected, virtual screening can be used as a robust methodology to explore molecular scaffolds that could act as inhibitors, guiding the development of new drug lead compounds. This review focuses on how the advent of omics and the development and application of bioinformatics strategies conduct a "big-data era" that improves target selection and lead compound identification in a cost-effective and shortened timeline.

BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling

G protein-coupled receptors (GPCRs) are the largest family of membrane proteins with more than 800 members. GPCRs are involved in numerous physiological functions within the human body and are the target of more than 30% of the United States Food and Drug Administration (FDA) approved drugs. At present, over 400 experimental GPCR structures are available in the Protein Data Bank (PDB) representing 76 unique receptors. The absence of an experimental structure for the majority of GPCRs demand homology models for structure-based drug discovery workflows. The generation of good homology models requires appropriate templates. The commonly used methods for template selection are based on sequence identity. However, there exists low sequence identity among the GPCRs. Sequences with similar patterns of hydrophobic residues are often structural homologs, even with low sequence identity. Extending this, we propose a biophysical approach for template selection based principally on hydrophobicity correspondence between the target and the template. Our approach takes into consideration other relevant parameters, including resolution, similarity within the orthosteric binding pocket of GPCRs, and structure completeness, for template selection. The proposed method was implemented in the form of a free tool called Bio-GATS, to provide the user with easy selection of the appropriate template for a query GPCR sequence. Bio-GATS was successfully validated with recent published benchmarking datasets. An application to an olfactory receptor to select an appropriate template has also been provided as a case study.

Vanishing white matter: Eukaryotic initiation factor 2B model and the impact of missense mutations

Background

Vanishing white matter (VWM) is a leukodystrophy, caused by recessive mutations in eukaryotic initiation factor 2B (eIF2B)‐subunit genes (EIF2B1–EIF2B5); 80% are missense mutations. Clinical severity is highly variable, with a strong, unexplained genotype–phenotype correlation.

Materials and Methods

With information from a recent natural history study, we severity‐graded 97 missense mutations. Using in silico modeling, we created a new human eIF2B model structure, onto which we mapped the missense mutations. Mutated residues were assessed for location in subunits, eIF2B complex, and functional domains, and for information on biochemical activity.

Results

Over 50% of mutations have (ultra‐)severe phenotypic effects. About 60% affect the ε‐subunit, containing the catalytic domain, mostly with (ultra‐)severe effects. About 55% affect subunit cores, with variable clinical severity. About 36% affect subunit interfaces, mostly with severe effects. Very few mutations occur on the external eIf2B surface, perhaps because they have minor functional effects and are tolerated. One external surface mutation affects eIF2B‐substrate interaction and is associated with ultra‐severe phenotype.

Conclusion

Mutations that lead to (ultra‐)severe disease mostly affect amino acids with pivotal roles in complex formation and function of eIF2B. Therapies for VWM are emerging and reliable mutation‐based phenotype prediction is required for propensity score matching for trials and in the future for individualized therapy decisions.

Molecular Dynamics Simulations of Channelrhodopsin Chimera, C1C2

Molecular dynamics (MD) simulations have been successfully used for modeling dynamic behavior of biologically relevant systems, such as ion channels in representative environments to decode protein structure-function relationships. Protocol presented here describes steps for generating input files and modeling a monomer of transmembrane cation channel, channelrhodopsin chimera (C1C2), in representative environment of 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine (DOPC) planar lipid bilayer, TIP3P water and ions (Na+ and Cl-) using molecular dynamics package NAMD, molecular graphics/analysis tool VMD, and other relevant tools. MD simulations of C1C2 were performed at 303.15 K and in constant particle number, isothermal-isobaric (NpT) ensemble. The results of modeling have helped understand how key interactions in the center of the C1C2 channel contribute to channel gating and subsequent solvent transport across the membrane.

Homology modeling in the time of collective and artificial intelligence

Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.

Repurposing of FDA-Approved Toremifene to Treat COVID-19 by Blocking the Spike Glycoprotein and NSP14 of SARS-CoV-2

The global pandemic of Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to the death of more than 675,000 worldwide and over 150,000 in the United States alone. However, there are currently no approved effective pharmacotherapies for COVID-19. Here, we combine homology modeling, molecular docking, molecular dynamics simulation, and binding affinity calculations to determine potential targets for toremifene, a selective estrogen receptor modulator which we have previously identified as a SARS-CoV-2 inhibitor. Our results indicate the possibility of inhibition of the spike glycoprotein by toremifene, responsible for aiding in fusion of the viral membrane with the cell membrane, via a perturbation to the fusion core. An interaction between the dimethylamine end of toremifene and residues Q954 and N955 in heptad repeat 1 (HR1) perturbs the structure, causing a shift from what is normally a long, helical region to short helices connected by unstructured regions. Additionally, we found a strong interaction between toremifene and the methyltransferase nonstructural protein (NSP) 14, which could be inhibitory to viral replication via its active site. These results suggest potential structural mechanisms for toremifene by blocking the spike protein and NSP14 of SARS-CoV-2, offering a drug candidate for COVID-19.

Systematic and Comparative Evaluation of Software Programs for Template-Based Modeling of Protein Structures

Modeling protein structures is critical for understanding protein functions in various biological and biotechnological studies. Among representative protein structure modeling approaches, template-based modeling (TBM) is by far the most reliable and most widely used approach to model protein structures. However, it still remains as a challenge to select appropriate software programs for pairwise alignments and model building, two major steps of the TBM. In this paper, pairwise alignment methods for TBM are first compared with respect to the quality of structure models built using these methods. This comparative study is conducted using comprehensive datasets, which cover 6185 domain sequences from Structural Classification of Proteins extended for soluble proteins, and 259 Protein Data Bank entries (whole protein sequences) from Orientations of Proteins in Membranes database for membrane proteins. Overall, a profile-based method, especially PSI-BLAST, consistently shows high performance across the datasets and model evaluation metrics used. Next, use of two model building programs, MODELLER and SWISS-MODEL, does not seem to significantly affect the quality of protein structure models built except for the Hard group (a group of relatively less homologous proteins) of membrane proteins. The results presented in this study will be useful for more accurate implementation of TBM.

Optogenetic Control of Spine-Head JNK Reveals a Role in Dendritic Spine Regression

In this study, we use an optogenetic inhibitor of c-Jun NH₂-terminal kinase (JNK) in dendritic spine sub-compartments of rat hippocampal neurons. We show that JNK inhibition exerts rapid (within seconds) reorganization of actin in the spine-head. Using real-time Förster resonance energy transfer (FRET) to measure JNK activity, we find that either excitotoxic insult (NMDA) or endocrine stress (corticosterone), activate spine-head JNK causing internalization of AMPARs and spine retraction. Both events are prevented upon optogenetic inhibition of JNK, and rescued by JNK inhibition even 2 h after insult. Moreover, we identify that the fast-acting anti-depressant ketamine reduces JNK activity in hippocampal neurons suggesting that JNK inhibition may be a downstream mediator of its anti-depressant effect. In conclusion, we show that JNK activation plays a role in triggering spine elimination by NMDA or corticosterone stress, whereas inhibition of JNK facilitates regrowth of spines even in the continued presence of glucocorticoid. This identifies that JNK acts locally in the spine-head to promote AMPAR internalization and spine shrinkage following stress, and reveals a protective function for JNK inhibition in preventing spine regression.

Analysis of tomato spotted wilt virus RNA-dependent RNA polymerase adaptative evolution and constrained domains using homology protein structure modelling

Tomato spotted wilt virus (TSWV; genus Orthotospovirus, family Tospoviridae) has a huge impact on a large range of plants worldwide. In this study, we determined the sequence of the large (L) RNA segment that encodes the RNA-dependent RNA polymerase (RdRp) from a TSWV isolate (LYE51) collected in the south of France. Analysis of the phylogenetic relationships of TSWV-LYE51 with other TSWV isolates shows that it is closely related to other European isolates. A 3D model of TSWV-LYE51 RdRp was built by homology with the RdRp structure of the La Crosse virus (genus Orthobunyavirus, family Peribunyaviridae). Finally, an analysis of positive and negative selection was carried out on 30 TSWV full-length RNA L sequences and compared with the phylogeny and the protein structure data. We showed that the seven codons that are under positive selection are distributed all along the RdRp gene. By contrast, the codons associated with negative selection are especially concentrated in three highly constrained domains: the endonuclease in charge of the cap-snatching mechanism, the thumb domain and the mid domain. Those three domains could constitute good candidates to look for host targets on which genetic resistance by loss of susceptibility could be developed.

Revisiting the "satisfaction of spatial restraints" approach of MODELLER for protein homology modeling

The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. The most widely employed tool to perform this task is MODELLER. This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. In this work, we have explored the idea of modifying MODELLER with two effective, yet computationally light strategies to improve its 3D modeling performance. Firstly, we have investigated how the level of accuracy in the estimation of structural variability between a target protein and its templates in the form of σ values profoundly influences 3D modeling. We show that the σ values produced by MODELLER are on average weakly correlated to the true level of structural divergence between target-template pairs and that increasing this correlation greatly improves the program’s predictions, especially in multiple-template modeling. Secondly, we have inquired into how the incorporation of statistical potential terms (such as the DOPE potential) in the MODELLER’s objective function impacts positively 3D modeling quality by providing a small but consistent improvement in metrics such as GDT-HA and lDDT and a large increase in stereochemical quality. Python modules to harness this second strategy are freely available at https://github.com/pymodproject/altmod. In summary, we show that there is a large room for improving MODELLER in terms of 3D modeling quality and we propose strategies that could be pursued in order to further increase its performance.

Proteins are fundamental biological molecules that carry out countless activities in living beings. Since the function of proteins is dictated by their three-dimensional atomic structures, acquiring structural details of proteins provides deep insights into their function. Currently, the most frequently used computational approach for protein structure prediction is template-based modeling. In this approach, a target protein is modeled using the experimentally-derived structural information of a template protein assumed to have a similar structure to the target. MODELLER is the most frequently used program for template-based 3D model building. Despite its success, its predictions are not always accurate enough to be useful in Biomedical Research. Here, we show that it is possible to greatly increase the performance of MODELLER by modifying two aspects of its algorithm. First, we demonstrate that providing the program with accurate estimations of local target-template structural divergence greatly increases the quality of its predictions. Additionally, we show that modifying MODELLER’s scoring function with statistical potential energetic terms also helps to improve modeling quality. This work will be useful in future research, since it reports practical strategies to improve the performance of this core tool in Structural Bioinformatics.

Crystal Structure Analysis and Conformational Epitope Mutation of Triosephosphate Isomerase, a Mud Crab Allergen

The triosephosphate isomerase (TIM), Scy p 8, is a crab allergen and shows cross-reactivity in the shellfish. Here, recombinant Scy p 8 was expressed, and its crystal structure was determined at a resolution of 1.8 Å. The three-dimensional structure of Scy p 8 is primarily composed of a (β/α)₈-barrel motif prototype. Additionally, Scy p 8 showed cross-reactivity with high sequential and secondary structural identity among TIMs from shellfish species. The site-directed mutagenesis of critical amino acids of conformational epitopes was carried out, and the mutants of Trp 168 and Lys 237 to Ala reduced immunoglobulin E (IgE)-binding activity by approximately 30%, compared with wild-type TIM in an inhibition ELISA; however, it still induced basophil activation despite the interpatient variability between patients. These results can help to provide an accurate template for the analysis of the IgE binding and establish meaningful relationships between structure and allergenicity.

Recent Trends in System-Scale Integrative Approaches for Discovering Protective Antigens Against Mycobacterial Pathogens

Mycobacterial infections are one of the deadliest infectious diseases still posing a major health burden worldwide. The battle against these pathogens needs to focus on novel approaches and key interventions. In recent times, availability of genome scale data has revolutionized the fields of computational biology and immunoproteomics. Here, we summarize the cutting-edge ‘omics’ technologies and innovative system scale strategies exploited to mine the available data. These may be targeted using high-throughput technologies to expedite the identification of novel antigenic candidates for the rational next generation vaccines and serodiagnostic development against mycobacterial pathogens for which traditional methods have been failing.

Homology modeling in drug discovery: Overview, current applications, and future perspectives

Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.

Current Therapeutic Molecules and Targets in Neurodegenerative Diseases Based on in silico Drug Design

Abstract: Background

As the number of elderly persons increases, neurodegenerative diseases are becoming ubiquitous. There is currently a great need for knowledge concerning management of old-age neurodegenerative diseases; the most important of which are: Alzheimer’s disease, Parkinson’s disease, Amyotrophic Lateral Sclerosis, and Huntington’s disease.

Objective

To summarize the potential of computationally predicted molecules and targets against neurodegenerative diseases.

Method

Review of literature published since 1997 against neurodegenerative diseases, utilizing as keywords: in silico, Alzheimer’s disease, Parkinson’s disease, Amyotrophic Lateral Sclerosis ALS, and Huntington’s disease was conducted.

Results and Conclusion

Due to the costs associated with experimentation and current ethical law, performing experiments directly on living organisms has become much more difficult. In this scenario, in silico techniques have been successful and have become powerful tools in the search to cure disease. Researchers use the Computer Aided Drug Design pipeline which: 1) generates 3-dimensional structures of target proteins through homology modeling 2) achieves stabilization through molecular dynamics simulation, and 3) exploits molecular docking through large compound libraries. Next generation sequencing is continually producing enormous amounts of raw sequence data while neuroimaging is producing a multitude of raw image data. To solve such pressing problems, these new tools and algorithms are required. This review elaborates precise in silico tools and techniques for drug targets, active molecules, and molecular docking studies, together with future prospects and challenges concerning possible breakthroughs in Alzheimer’s, Parkinson’s, Amyotrophic Lateral Sclerosis, and Huntington’s disease.

A Comparative Study of Modern Homology Modeling Algorithms for Rhodopsin Structure Prediction

Rhodopsins are seven α-helical membrane proteins that are of great importance in chemistry, biology, and modern biotechnology. Any in silico study on rhodopsin properties and functioning requires a high-quality three-dimensional structure. Due to particular difficulties with obtaining membrane protein structures from the experiment, in silico prediction of the three-dimensional rhodopsin structure based only on its primary sequence is an especially important task. For the last few years, significant progress was made in the field of protein structure prediction, especially for methods based on comparative modeling. However, the majority of this progress was made for soluble proteins and further investigations are needed to achieve similar progress for membrane proteins. In this paper, we evaluate the performance of modern protein structure prediction methodologies (implemented in the Medeller, I-TASSER, and Rosetta packages) for their ability to predict rhodopsin structures. Three widely used methodologies were considered: two general methodologies that are commonly applied to soluble proteins and a methodology that uses constraints that are specific for membrane proteins. The test pool consisted of 36 target-template pairs with different sequence similarities that was constructed on the basis of 24 experimental rhodopsin structures taken from the RCSB database. As a result, we showed that all three considered methodologies allow obtaining rhodopsin structures with the quality that is close to the crystallographic one (root mean square deviation (RMSD) of the predicted structure from the corresponding X-ray structure up to 1.5 Å) if the target-template sequence identity is higher than 40%. Moreover, all considered methodologies provided structures of average quality (RMSD < 4.0 Å) if the target-template sequence identity is higher than 20%. Such structures can be subsequently used for further investigation of molecular mechanisms of protein functioning and for the development of modern protein-based biotechnologies.

Chemical shift-based methods in NMR structure determination

Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.

Online Molecular Docking Resources

This chapter aims to present the available online resources that are used for protein modeling with accent to online molecular docking resources. SwissDock, MTiAutoDock, and PatchDock online docking tools are described and a few illustrative examples concerning the molecular docking studies for the cytochrom P450 interactions with the fungicide difenoconazole. The results obtained using different servers based on distinct approaches are compared and the advantages and/or disadvantages of every server are illustrated.

Computer Simulations of Peptides from the p53 DNA Binding Domain

We have studied the dynamics and thermodynamics of two of the four evolutionarily conserved segments from the p53 DNA binding domain, using molecular dynamics and replica exchange simulations. These two regions contain well-defined elements of secondary structure (a β hairpin for region II and an α helix for region V) and bind to DNA in the intact protein. They are also mutational hot spots. The goal of our study was to determine the stability and folding propensity of these peptides in isolation. We used three force fields and solvent models (CHARMM19 with EEF1, CHARMM27 with GBMV, GROMOS96 with SPC). The predicted stability, folding propensity, and secondary structures depend upon the potential. Secondary structure predictors identify helical propensity for region II, in agreement with one of the force fields (CHARMM/GBMV). However, the other two potentials favor β structure for this peptide, although the conformations may differ from the crystal. For region V secondary structure predictions are unclear. Only one force field (CHARMM/GBMV) produces low-lying free energy minima that retain some of the α helical structure from the crystal structure. The other two potentials appear to favor β structure for this peptide.

Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling

Homology modeling predicts the 3D structure of a query protein based on the sequence alignment with one or more template proteins of known structure. Its great importance for biological research is owed to its speed, simplicity, reliability and wide applicability, covering more than half of the residues in protein sequence space. Although multiple templates have been shown to generally increase model quality over single templates, the information from multiple templates has so far been combined using empirically motivated, heuristic approaches.

We present here a rigorous statistical framework for multi-template homology modeling. First, we find that the query proteins’ atomic distance restraints can be accurately described by two-component Gaussian mixtures. This insight allowed us to apply the standard laws of probability theory to combine restraints from multiple templates. Second, we derive theoretically optimal weights to correct for the redundancy among related templates. Third, a heuristic template selection strategy is proposed.

We improve the average GDT-ha model quality score by 11% over single template modeling and by 6.5% over a conventional multi-template approach on a set of 1000 query proteins. Robustness with respect to wrong constraints is likewise improved. We have integrated our multi-template modeling approach with the popular MODELLER homology modeling software in our free HHpred server http://toolkit.tuebingen.mpg.de/hhpred and also offer open source software for running MODELLER with the new restraints at https://bitbucket.org/soedinglab/hh-suite.

Since a protein’s function is largely determined by its structure, predicting a protein’s structure from its amino acid sequence can be very useful to understand its molecular functions and its role in biological pathways. By far the most widely used computational approach for protein structure prediction relies on detecting a homologous relationship with a protein of known structure and using this protein as a template to model the structure of the query protein on it. The basic concepts of this homology modelling approach have not changed during the last 20 years. In this study we extend the probabilistic formulation of homology modelling to the consistent treatment of multiple templates. Our new theoretical approach allowed us to improve the quality of homology models by 11% over a baseline single-template approach and by 6.5% over a multi-template approach.

Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment

The 2013/2014 Community Structure-Activity Resource (CSAR) challenge was designed to prospectively validate advancement in the field of docking and scoring receptor-small molecule interactions. Purely computational methods have been found to be quite limiting. Thus, the challenges assessed methods that combined both experimental data and computational approaches. Here, we describe our contribution to solve three important challenges in rational drug discovery: rank-ordering protein primary sequences based on affinity to a compound, determining close-to-native bound conformations out of a set of decoy poses, and rank-ordering sets of congeneric compounds based on affinity to a given protein. We showed that the most significant contribution to a meaningful enrichment of native-like models was the identification of the best receptor structure for docking and scoring. Depending on the target, the optimal receptor for cross-docking and scoring was identified by a self-consistent docking approach that used the Vina scoring function, by aligning compounds to the closest cocrystal or by selecting the cocrystal receptor with the largest pocket. For tRNA (m1G37) methyltransferase (TRMD), ranking a set of 31 congeneric binding compounds cross-docked to the optimal receptor resulted in a R(2) = 0.67; whereas, using any other of the 13 receptor structures led to almost no enrichment of native-like complex structures. Furthermore, although redocking predicted lower RMSDs relative to the bound structures, the ranking based on multiple receptor structures did not improve the correlation coefficient. Our predictions highlight the role of rational structure-based modeling in maximizing the outcome of virtual screening, as well as limitations scoring multiple receptors.

A robust all-atom model for LCAT generated by homology modeling

LCAT is activated by apoA-I to form cholesteryl ester. We combined two structures, phospholipase A2 (PLA2) that hydrolyzes the ester bond at the sn-2 position of oxidized (short) acyl chains of phospholipid, and bacteriophage tubulin PhuZ, as C- and N-terminal templates, respectively, to create a novel homology model for human LCAT. The juxtaposition of multiple structural motifs matching experimental data is compelling evidence for the general correctness of many features of the model: i) The N-terminal 10 residues of the model, required for LCAT activity, extend the hydrophobic binding trough for the sn-2 chain 15-20 Å relative to PLA2. ii) The topography of the trough places the ester bond of the sn-2 chain less than 5 Å from the hydroxyl of the catalytic nucleophile, S181. iii) A β-hairpin resembling a lipase lid separates S181 from solvent. iv) S181 interacts with three functionally critical residues: E149, that regulates sn-2 chain specificity, and K128 and R147, whose mutations cause LCAT deficiency. Because the model provides a novel explanation for the complicated thermodynamic problem of the transfer of hydrophobic substrates from HDL to the catalytic triad of LCAT, it is an important step toward understanding the antiatherogenic role of HDL in reverse cholesterol transport.

Plant-PrAS: a database of physicochemical and structural properties and novel functional regions in plant proteomes

Arabidopsis thaliana is an important model species for studies of plant gene functions. Research on Arabidopsis has resulted in the generation of high-quality genome sequences, annotations and related post-genomic studies. The amount of annotation, such as gene-coding regions and structures, is steadily growing in the field of plant research. In contrast to the genomics resource of animals and microorganisms, there are still some difficulties with characterization of some gene functions in plant genomics studies. The acquisition of information on protein structure can help elucidate the corresponding gene function because proteins encoded in the genome possess highly specific structures and functions. In this study, we calculated multiple physicochemical and secondary structural parameters of protein sequences, including length, hydrophobicity, the amount of secondary structure, the number of intrinsically disordered regions (IDRs) and the predicted presence of transmembrane helices and signal peptides, using a total of 208,333 protein sequences from the genomes of six representative plant species, Arabidopsis thaliana, Glycine max (soybean), Populus trichocarpa (poplar), Oryza sativa (rice), Physcomitrella patens (moss) and Cyanidioschyzon merolae (alga). Using the PASS tool and the Rosetta Stone method, we annotated the presence of novel functional regions in 1,732 protein sequences that included unannotated sequences from the Arabidopsis and rice proteomes. These results were organized into the Plant Protein Annotation Suite database (Plant-PrAS), which can be freely accessed online at http://plant-pras.riken.jp/.

Computational design of selective peptides to discriminate between similar PDZ domains in an oncogenic pathway

Reagents that target protein-protein interactions to rewire signaling are of great relevance in biological research. Computational protein design may offer a means of creating such reagents on demand, but methods for encoding targeting selectivity are sorely needed. This is especially challenging when targeting interactions with ubiquitous recognition modules--for example, PDZ domains, which bind C-terminal sequences of partner proteins. Here we consider the problem of designing selective PDZ inhibitor peptides in the context of an oncogenic signaling pathway, in which two PDZ domains (NHERF-2 PDZ2-N2P2 and MAGI-3 PDZ6-M3P6) compete for a receptor C-terminus to differentially modulate oncogenic activities. Because N2P2 has been shown to increase tumorigenicity and M3P6 to decreases it, we sought to design peptides that inhibit N2P2 without affecting M3P6. We developed a structure-based computational design framework that models peptide flexibility in binding yet is efficient enough to rapidly analyze tradeoffs between affinity and selectivity. Designed peptides showed low-micromolar inhibition constants for N2P2 and no detectable M3P6 binding. Peptides designed for reverse discrimination bound M3P6 tighter than N2P2, further testing our technology. Experimental and computational analysis of selectivity determinants revealed significant indirect energetic coupling in the binding site. Successful discrimination between N2P2 and M3P6, despite their overlapping binding preferences, is highly encouraging for computational approaches to selective PDZ targeting, especially because design relied on a homology model of M3P6. Still, we demonstrate specific deficiencies of structural modeling that must be addressed to enable truly robust design. The presented framework is general and can be applied in many scenarios to engineer selective targeting.

Backbone dependency further improves side chain prediction efficiency in the Energy-based Conformer Library (bEBL)

Side chain optimization is an integral component of many protein modeling applications. In these applications, the conformational freedom of the side chains is often explored using libraries of discrete, frequently occurring conformations. Because side chain optimization can pose a computationally intensive combinatorial problem, the nature of these conformer libraries is important for ensuring efficiency and accuracy in side chain prediction. We have previously developed an innovative method to create a conformer library with enhanced performance. The Energy-based Library (EBL) was obtained by analyzing the energetic interactions between conformers and a large number of natural protein environments from crystal structures. This process guided the selection of conformers with the highest propensity to fit into spaces that should accommodate a side chain. Because the method requires a large crystallographic data-set, the EBL was created in a backbone-independent fashion. However, it is well established that side chain conformation is strongly dependent on the local backbone geometry, and that backbone-dependent libraries are more efficient in side chain optimization. Here we present the backbone-dependent EBL (bEBL), whose conformers are independently sorted for each populated region of Ramachandran space. The resulting library closely mirrors the local backbone-dependent distribution of side chain conformation. Compared to the EBL, we demonstrate that the bEBL uses fewer conformers to produce similar side chain prediction outcomes, thus further improving performance with respect to the already efficient backbone-independent version of the library.

A novel motif in the NaTrxh N-terminus promotes its secretion, whereas the C-terminus participates in its interaction with S-RNase in vitro

BACKGROUND

NaTrxh, a thioredoxin type h, shows differential expression between self-incompatible and self-compatible Nicotiana species. NaTrxh interacts in vitro with S-RNase and co-localizes with it in the extracellular matrix of the stylar transmitting tissue. NaTrxh contains N- and C-terminal extensions, a feature shared by thioredoxin h proteins of subgroup 2. To ascertain the function of these extensions in NaTrxh secretion and protein-protein interaction, we performed a deletion analysis on NaTrxh and fused the resulting variants to GFP.

RESULTS

We found an internal domain in the N-terminal extension, called Nβ, that is essential for NaTrxh secretion but is not hydrophobic, a canonical feature of a signal peptide. The lack of hydrophobicity as well as the location of the secretion signal within the NaTrxh primary structure, suggest an unorthodox secretion route for NaTrxh. Notably, we found that the fusion protein NaTrxh-GFP(KDEL) is retained in the endoplasmic reticulum and that treatment of NaTrxh-GFP-expressing cells with Brefeldin A leads to its retention in the Golgi, which indicates that NaTrxh uses, to some extent, the endoplasmic reticulum and Golgi apparatus for secretion. Furthermore, we found that Nβ contributes to NaTrxh tertiary structure stabilization and that the C-terminus functions in the protein-protein interaction with S-RNase.

CONCLUSIONS

The extensions contained in NaTrxh sequence have specific functions on the protein. While the C-terminus directly participates in protein-protein interaction, particularly on its interaction with S-RNase in vitro; the N-terminal extension contains two structurally different motifs: Nα and Nβ. Nβ, the inner domain (Ala-17 to Pro-27), is essential and enough to target NaTrxh towards the apoplast. Interestingly, when it was fused to GFP, this protein was also found in the cell wall of the onion cells. Although the biochemical features of the N-terminus suggested a non-classical secretion pathway, our results provided evidence that NaTrxh at least uses the endoplasmic reticulum, Golgi apparatus and also vesicles for secretion. Therefore, the Nβ domain sequence is suggested to be a novel signal peptide.

Molecular modeling and ligand docking for solute carrier (SLC) transporters

Solute Carrier (SLC) transporters are membrane proteins that transport solutes, such as ions, metabolites, peptides, and drugs, across biological membranes, using diverse energy coupling mechanisms. In human, there are 386 SLC transporters, many of which contribute to the absorption, distribution, metabolism, and excretion of drugs and/or can be targeted directly by therapeutics. Recent atomic structures of SLC transporters determined by X-ray crystallography and NMR spectroscopy have significantly expanded the applicability of structure-based prediction of SLC transporter ligands, by enabling both comparative modeling of additional SLC transporters and virtual screening of small molecules libraries against experimental structures as well as comparative models. In this review, we begin by describing computational tools, including sequence analysis, comparative modeling, and virtual screening, that are used to predict the structures and functions of membrane proteins such as SLC transporters. We then illustrate the applications of these tools to predicting ligand specificities of select SLC transporters, followed by experimental validation using uptake kinetic measurements and other assays. We conclude by discussing future directions in the discovery of the SLC transporter ligands.

Comparative Modeling of Drug Target Proteins☆

In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples.

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.

ModBase, a database of annotated comparative protein structure models and associated resources

ModBase (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence-structure alignment, model building and model assessment (http://salilab.org/modeller/). ModBase currently contains almost 30 million reliable models for domains in 4.7 million unique protein sequences. ModBase allows users to compute or update comparative models on demand, through an interface to the ModWeb modeling server (http://salilab.org/modweb). ModBase models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/). Recently developed associated resources include the AllosMod server for modeling ligand-induced protein dynamics (http://salilab.org/allosmod), the AllosMod-FoXS server for predicting a structural ensemble that fits an SAXS profile (http://salilab.org/allosmod-foxs), the FoXSDock server for protein–protein docking filtered by an SAXS profile (http://salilab.org/foxsdock), the SAXS Merge server for automatic merging of SAXS profiles (http://salilab.org/saxsmerge) and the Pose & Rank server for scoring protein–ligand complexes (http://salilab.org/poseandrank). In this update, we also highlight two applications of ModBase: a PSI:Biology initiative to maximize the structural coverage of the human alpha-helical transmembrane proteome and a determination of structural determinants of human immunodeficiency virus-1 protease specificity.

Identification of an allosteric pocket on human hsp70 reveals a mode of inhibition of this therapeutically important protein

Hsp70s are important cancer chaperones that act upstream of Hsp90 and exhibit independent anti-apoptotic activities. To develop chemical tools for the study of human Hsp70, we developed a homology model that unveils a previously unknown allosteric site located in the nucleotide binding domain of Hsp70. Combining structure-based design and phenotypic testing, we discovered a previously unknown inhibitor of this site, YK5. In cancer cells, this compound is a potent and selective binder of the cytosolic but not the organellar human Hsp70s and has biological activity partly by interfering with the formation of active oncogenic Hsp70/Hsp90/client protein complexes. YK5 is a small molecule inhibitor rationally designed to interact with an allosteric pocket of Hsp70 and represents a previously unknown chemical tool to investigate cellular mechanisms associated with Hsp70.

Mass spectrometry coupled experiments and protein structure modeling methods

With the accumulation of next generation sequencing data, there is increasing interest in the study of intra-species difference in molecular biology, especially in relation to disease analysis. Furthermore, the dynamics of the protein is being identified as a critical factor in its function. Although accuracy of protein structure prediction methods is high, provided there are structural templates, most methods are still insensitive to amino-acid differences at critical points that may change the overall structure. Also, predicted structures are inherently static and do not provide information about structural change over time. It is challenging to address the sensitivity and the dynamics by computational structure predictions alone. However, with the fast development of diverse mass spectrometry coupled experiments, low-resolution but fast and sensitive structural information can be obtained. This information can then be integrated into the structure prediction process to further improve the sensitivity and address the dynamics of the protein structures. For this purpose, this article focuses on reviewing two aspects: the types of mass spectrometry coupled experiments and structural data that are obtainable through those experiments; and the structure prediction methods that can utilize these data as constraints. Also, short review of current efforts in integrating experimental data in the structural modeling is provided.

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.

Crystal structure of peanut (Arachis hypogaea) allergen Ara h 5

Profilins from numerous species are known to be allergens, including food allergens, such as peanut ( Arachis hypogaea ) allergen Ara h 5, and pollen allergens, such as birch allergen Bet v 2. Patients with pollen allergy can also cross-react to peanut. Structural characterization of allergens will allow a better understanding of the allergenicity of food allergens and their cross-reactivities. The three-dimensional structures of most known food allergens remain to be elucidated. Here, we report the first crystallographic study of a food allergen in the profilin family. The structure of peanut allergen Ara h 5 was determined, and the resolution of the final refined structure was 1.1 Å. Structure alignment revealed that Ara h 5 is more similar to Bet v 2 than to Hev b 8, although sequence alignment suggested that Ara h 5 is more closely related to Hev b 8 than to Bet v 2, indicating that homology-model-based prediction of immunoglobulin E epitopes needs to be interpreted with caution.

Bioinformatics in Retina

Bioinformatics, a word coined for the applications of computer science in biology, is now promising as a major constituent in modern biology and biomedical research. Bioinformatics plays an important role for the integration of broad disciplines of biology to understand the complex mechanisms of the cell. Bioinformatics also aids the way in which biomedical investigators use the information in their testing. Development and implementation of this novel field enable efficient access and management of different types of biological information including those at the genomic, proteomic, and metabolomic level to understand about disease mechanisms and identify new molecular targets for drug discovery. Bioinformatics has expanded its wings in exploring out different important contributions in relation with medical sciences such as neurology, parasitology, hematology, and pathology including ophthalmology. Many bioinformatics-oriented studies have contributed a lot in ophthalmology and given birth to new avenues of occuloinformatics, hence, a new coined term, occuloinformatics: a new approach of research and diagnostics related to ocular disorders with significant inputs of bioinformatics. In this current review, we tried to focus on current avenues and significant contributions of bioinformatics with special reference to retinal disorders.

Homology modeling a fast tool for drug discovery: current perspectives

Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery.