Detection and Analysis of Short Linear Motif-Based Protein-Protein Interactions with SLiMAn2 Web Server.

Interactomics is bringing a deluge of data regarding protein-protein interactions (PPIs) which are involved in various molecular processes in all types of cells. However, this information does not easily translate into direct and precise molecular interfaces. This limits our understanding of each interaction network and prevents their efficient modulation. A lot of the detected interactions involve recognition of short linear motifs (SLiMs) by a folded domain while others rely on domain-domain interactions. Functional SLiMs hide among a lot of spurious ones, making deeper analysis of interactomes tedious. Hence, actual contacts and direct interactions are difficult to identify.Consequently, there is a need for user-friendly bioinformatic tools, enabling rapid molecular and structural analysis of SLiM-based PPIs in a protein network. In this chapter, we describe the use of the new webserver SLiMAn to help digging into SLiM-based PPIs in an interactive fashion.

Structural and functional characterization of peste des petits ruminants virus coded hemagglutinin protein using various in-silico approaches.

Peste des petits ruminants (PPR), a disease of socioeconomic importance has been a serious threat to small ruminants. The causative agent of this disease is PPR virus (PPRV) which belongs to the genus Morbillivirus. Hemagglutinin (H) is a PPRV coded transmembrane protein embedded in the viral envelope and plays a vital role in mediating the entry of virion particle into the cell. The infected host mounts an effective humoral response against H protein which is important for host to overcome the infection. In the present study, we have investigated structural, physiological and functional properties of hemagglutinin protein using various computational tools. The sequence analysis and structure prediction analysis show that hemagglutinin protein comprises of beta sheets as the predominant secondary structure, and may lack neuraminidase activity. PPRV-H consists of several important domains and motifs that form an essential scaffold which impart various critical roles to the protein. Comparative modeling predicted the protein to exist as a homo-tetramer that binds to its cognate cellular receptors. Certain amino acid substitutions identified by multiple sequence alignment were found to alter the predicted structure of the protein. PPRV-H through its predicted interaction with TLR-2 molecule may drive the expression of CD150 which could further propagate the virus into the host. Together, our study provides new insights into PPRV-H protein structure and its predicted functions.

Estudo estrutural e comparativo entre Sirtuínas 2 de diferentes espécies de Leishmania / Structural and comparative study between Sirtuins 2 of different species of Leishmania by means of Comparative Modeling and Molecular Dynamic

As leishmanioses são doenças negligenciadas que afetam mais de um bilhão e meio de pessoas ao redor do mundo, principalmente nos países em desenvolvimento, provocando grandes impactos socioeconômicos. Os fármacos disponíveis para o tratamento dessas doenças são ineficazes e apresentam graves efeitos adversos. O processo de pesquisa de novos fármacos envolve, entre outras coisas, a seleção de alvos bioquímicos essenciais para a sobrevivência e desenvolvimento do agente causador. Neste sentido, a Sirtuína 2, uma enzima epigenética com atividade hidrolase essencial para a sobrevivência dos parasitas do gênero Leishmania se apresenta como um alvo validado na busca de novos fármacos contra essas parasitoses. O planejamento de fármacos baseado na estrutura do receptor requer o conhecimento da estrutura tridimensional da proteína alvo. Desta forma, a elucidação estrutural e um estudo minucioso das Sirtuínas das várias espécies do gênero Leishmania apresenta-se como uma importante abordagem na aplicação desta estratégia na busca por agentes quimioterápicos. Até o momento, na família Trypanosomatidae, a única estrutura tridimensional resolvida experimentalmente de uma enzima Sirtuína 2 é a da espécie L. infantum. Assim, este trabalho aplicou a abordagem de Modelagem Comparativa utilizando o software Modeller na construção de modelos da Sir2rp1 das espécies L. infantum, L. major e L. braziliensis, cujas sequências de aminoácidos foram extraídas do banco de dados UNIProt. Os modelos construídos foram validados por meio da função de escore DOPE do Modeller e dos servidores PROCHECK, MolProbity e QMEAN, avaliando sua qualidade estereoquímica e seu enovelamento. Os ligantes naturais da enzima foram sobrepostos nos modelos construídos por alinhamento estrutural utilizando o software PyMol e os complexos validados foram submetidos a simulações de Dinâmica Molecular através do pacote GROMACS. Os complexos refinados foram então analisados por meio dos softwares PyMol e LigPlotPlus e dos pacotes GROMACS e gmx_MMPBSA, e foram estudados os sítios de ligação dos substratos e os resíduos de aminoácidos relevantes envolvidos em sua ligação e reconhecimento. A Modelagem Comparativa da Sirtuína 2 humana e seus homólogos das espécies L. infantum, L. major e L. braziliensis, as simulações de Dinâmica Molecular realizadas com os modelos enzimáticos construídos e validados complexados com seus ligantes naturais, os cálculos de energia de interação entre os modelos e seus substratos e o estudo estrutural comparativo realizado entre eles nos fornecem uma base teórica para a busca de novos inibidores da Sirtuína 2 que sejam mais seletivos e potentes contra as enzimas parasitárias, abrindo caminho para o desenvolvimento de candidatos a fármacos leishmanicidas mais seguros e eficazes

Leishmaniasis are neglected diseases that affect more than one and a half billion people around the world, mainly in developing countries, causing major socioeconomic impacts. The drugs available for the treatment of these diseases are ineffective and have serious adverse effects. The process of researching new drugs involves, among other things, the selection of biochemical targets essential for the survival and development of the causative agent. In this sense, Sirtuin 2, an epigenetic enzyme with hydrolase activity essential for the survival of parasites of the Leishmania genus, presents itself as a validated target in the search for new drugs against these parasites. Structure-Based Drug Design requires knowledge of the three-dimensional structure of the target protein. In this way, structural elucidation and a detailed study of Sirtuins from various species of the genus Leishmania presents itself as an important approach in the application of this strategy in the search for chemotherapeutic agents. To date, in the Trypanosomatidae family, the only experimentally resolved three-dimensional structure of a Sirtuin 2 enzyme is that of the species L. infantum. Thus, this work applied the Comparative Modeling approach using the Modeller software in the construction of Sir2rp1 models of the species L. infantum, L. major and L. braziliensis, whose amino acid sequences were retrieved from the UNIProt database. The constructed models were validated using Modeller's DOPE score function and the PROCHECK, MolProbity and QMEAN servers, evaluating their stereochemical quality and folding. The enzyme's natural ligands were superimposed on the built models by structural alignment using the PyMol software and the validated complexes were subjected to Molecular Dynamics simulations using the GROMACS package. The refined complexes were then analyzed using the PyMol and LigPlotPlus softwares and the GROMACS and gmx_MMPBSA packages, and the substrate binding sites and relevant amino acid residues involved in their binding and recognition were studied. The Comparative Modeling of human Sirtuin 2 and its homologues from the species L. infantum, L. major and L. braziliensis, the Molecular Dynamics simulations carried out with the constructed and validated enzymatic models complexed with their natural ligands, the interaction energy calculations between the models and their substrates and the comparative structural study carried out between them provide us with a theoretical basis for the search for new Sirtuin 2 inhibitors that are more selective and potent against the parasitic enzymes, paving the way for the development of safer and more effective leishmanicidal drug candidates

Development, validation and analysis of a human profurin 3D model using comparative modeling and molecular dynamics simulations.

The emergence of new viruses can lead to the outbreak of pandemics as occurred at the end of 2019 with the coronavirus disease (or COVID-19). The fastest way to effectively control viral infections is to develop broad-spectrum antivirals that can fight at least an entire class of viruses. Profurin, the furin precursor propeptide, is responsible for the autoactivation step which is crucial for the maturation of several viral substrates. This role makes the study of furin and profurin interactions interesting for the development of new potential broad-spectrum antivirals for the treatment against several human viral diseases. Since there is no 3D model of profurin published in the literature or deposited in a database, this work reports the development, validation and analysis of a profurin 3D model using comparative modeling and molecular dynamics. The model is available in ModelArchive at https://www.modelarchive.org/doi/10.5452/ma-ct8l7. The usage of this model will make possible further studies of molecular docking and MD simulations of the profurin-furin system, in the design of new potential broad-spectrum antivirals for the treatment against several human viral diseases.Communicated by Ramaswamy H. Sarma.

Assessment of the low-field magnetic resonance imaging for the brain scan imaging of the infant hydrocephalus.

Objective: In today's global clinical settings, low-field magnetic resonance imaging (MRI) technology is becoming increasingly prevalent. Ensuring high-quality image acquisition is crucial for accurate disease diagnosis and treatment and for evaluating the impact of poor-quality images. In this study, we explored the potential of deep learning as a diagnostic tool for improving image quality in hydrocephalus analysis planning. This could include discussions on the diagnostic accuracy, cost-effectiveness, and practicality of using low-field MRI as an alternative. Methods: There are many reasons which are going to affect infant computed tomography images. These are spatial resolution, noise, and contrast between the brain and cerebrospinal fluid (CSF). Now, we can enhance using the application of deep learning algorithms. Both improved and down quality were situated to the three qualified pediatric neurosurgeons comfortable with working in poor- to middle-level income countries for the analysis of clinical tools in the planning of the treatment of hydrocephalus. Results: The results predict that a picture will be classified as beneficial for hydrocephalus treatment planning, according to image resolution and the contrast-to-noise ratio (CNR) between the brain and CSF. The CNR is significantly improved by deep learning enhancement, which also improves the apparent likelihood of the image. Conclusion: However, poor-quality images might be desirable to image improved by deep learning, since those images will not offer the risk of confusing facts which could misguide the decision of the analysis of patients. Such findings support the newly introduced measurement standards in estimating the acceptable quality of images for clinical use.

Projected long-term effects of colorectal cancer screening disruptions following the COVID-19 pandemic.

The aftermath of the initial phase of the COVID-19 pandemic may contribute to the widening of disparities in colorectal cancer (CRC) outcomes due to differential disruptions to CRC screening. This comparative microsimulation analysis uses two CISNET CRC models to simulate the impact of ongoing screening disruptions induced by the COVID-19 pandemic on long-term CRC outcomes. We evaluate three channels through which screening was disrupted: delays in screening, regimen switching, and screening discontinuation. The impact of these disruptions on long-term CRC outcomes was measured by the number of life-years lost due to CRC screening disruptions compared to a scenario without any disruptions. While short-term delays in screening of 3-18 months are predicted to result in minor life-years loss, discontinuing screening could result in much more significant reductions in the expected benefits of screening. These results demonstrate that unequal recovery of screening following the pandemic can widen disparities in CRC outcomes and emphasize the importance of ensuring equitable recovery to screening following the pandemic.

In Silico Analysis of a GH3 ß-Glucosidase from Microcystis aeruginosa CACIAM 03.

Cyanobacteria are rich sources of secondary metabolites and have the potential to be excellent industrial enzyme producers. ß-glucosidases are extensively employed in processing biomass degradation as they mediate the most crucial step of bioconversion of cellobiose (CBI), hence controlling the efficiency and global rate of biomass hydrolysis. However, the production and availability of these enzymes derived from cyanobacteria remains limited. In this study, we evaluated the ß-glucosidase from Microcystis aeruginosa CACIAM 03 (MaBgl3) and its potential for bioconversion of cellulosic biomass by analyzing primary/secondary structures, predicting physicochemical properties, homology modeling, molecular docking, and simulations of molecular dynamics (MD). The results showed that MaBgl3 derives from an N-terminal domain folded as a distorted ß-barrel, which contains the conserved His-Asp catalytic dyad often found in glycosylases of the GH3 family. The molecular docking results showed relevant interactions with Asp81, Ala271 and Arg444 residues that contribute to the binding process during MD simulation. Moreover, the MD simulation of the MaBgl3 was stable, shown by analyzing the root mean square deviation (RMSD) values and observing favorable binding free energy in both complexes. In addition, experimental data suggest that MaBgl3 could be a potential enzyme for cellobiose-hydrolyzing degradation.

Easy Not Easy: Comparative Modeling with High-Sequence Identity Templates.

Homology modeling is the most common technique to build structural models of a target protein based on the structure of proteins with high-sequence identity and available high-resolution structures. This technique is based on the idea that protein structure shows fewer changes than sequence through evolution. While in this scenario single mutations would minimally perturb the structure, experimental evidence shows otherwise: proteins with high conformational diversity impose a limit of the paradigm of comparative modeling as the same protein sequence can adopt dissimilar three-dimensional structures. These cases present challenges for modeling; at first glance, they may seem to be easy cases, but they have a complexity that is not evident at the sequence level. In this chapter, we address the following questions: Why should we care about conformational diversity? How to consider conformational diversity when doing template-based modeling in a practical way?

Specificities of Modeling of Membrane Proteins Using Multi-Template Homology Modeling.

Structures of membrane proteins are challenging to determine experimentally and currently represent only about 2% of the structures in the Protein Data Bank. Because of this disparity, methods for modeling membrane proteins are fewer and of lower quality than those for modeling soluble proteins. However, better expression, crystallization, and cryo-EM techniques have prompted a recent increase in experimental structures of membrane proteins, which can act as templates to predict the structure of closely related proteins through homology modeling. Because homology modeling relies on a structural template, it is easier and more accurate than fold recognition methods or de novo modeling, which are used when the sequence similarity between the query sequence and the sequence of related proteins in structural databases is below 25%. In homology modeling, a query sequence is mapped onto the coordinates of a single template and refined. With the increase in available templates, several templates often cover overlapping segments of the query sequence. Multi-template modeling can be used to identify the best template for local segments and join them into a single model. Here we provide a protocol for modeling membrane proteins from multiple templates in the Rosetta software suite. This approach takes advantage of several integrated frameworks, namely, RosettaScripts, RosettaCM, and RosettaMP with the membrane scoring function.

Homology Modeling of the G Protein-Coupled Receptors.

G protein-coupled receptors (GPCRs) are therapeutically important family of membrane proteins. Despite growing number of experimental structures available for GPCRs, homology modeling remains a relevant method for studying these receptors and for discovering new ligands for them. Here we describe the state-of-the-art methods for modeling GPCRs, starting from template selection, through fine-tuning sequence alignment to model refinement.

CM2D3: Furnishing the Human Interactome with Structural Models of Protein Complexes Derived by Comparative Modeling and Docking.

The human interactome is composed of around half a million interactions according to recent estimations and it is only for a small fraction of those that three-dimensional structural information is available. Indeed, the structural coverage of the human interactome is very low and given the complexity and time-consuming requirements of solving protein structures this problem will remain for the foreseeable future. Structural models, or predictions, of protein complexes can provide valuable information when the experimentally determined 3D structures are not available. Here we present CM2D3, a relational database containing structural models of the whole human interactome derived both from comparative modeling and data-driven docking. Starting from a consensus interactome derived from integrating several interactomics databases, a strategy was devised to derive structural models by computational means. Currently, CM2D3 includes 33338 structural models of which 5121 derived from comparative modeling and the remaining from docking. Of the latter, the structures of 14554 complexes were derived from monomers modeled by M4T while the rest were modeled with structures as predicted by AlphaFold2. Lastly, CM2D3 complements existing resources by focusing on models derived from both free-docking, as opposed to template-based docking, and hence expanding the available structural information on protein complexes to the scientific community. Database URL:http://www.bioinsilico.org/CM2D3.

Comparative Modeling and Analysis of Extremophilic D-Ala-D-Ala Carboxypeptidases.

Understanding the molecular adaptations of organisms to extreme environments requires a comparative analysis of protein structure, function, and dynamics across species found in different environmental conditions. Computational studies can be particularly useful in this pursuit, allowing exploratory studies of large numbers of proteins under different thermal and chemical conditions that would be infeasible to carry out experimentally. Here, we perform such a study of the MEROPS family S11, S12, and S13 proteases from psychophilic, mesophilic, and thermophilic bacteria. Using a combination of protein structure prediction, atomistic molecular dynamics, and trajectory analysis, we examine both conserved features and trends across thermal groups. Our findings suggest a number of hypotheses for experimental investigation.

Integrating comparative modeling and accelerated simulations reveals conformational and energetic basis of actomyosin force generation.

Muscle contraction is performed by arrays of contractile proteins in the sarcomere. Serious heart diseases, such as cardiomyopathy, can often be results of mutations in myosin and actin. Direct characterization of how small changes in the myosin-actin complex impact its force production remains challenging. Molecular dynamics (MD) simulations, although capable of studying protein structure-function relationships, are limited owing to the slow timescale of the myosin cycle as well as a lack of various intermediate structures for the actomyosin complex. Here, employing comparative modeling and enhanced sampling MD simulations, we show how the human cardiac myosin generates force during the mechanochemical cycle. Initial conformational ensembles for different myosin-actin states are learned from multiple structural templates with Rosetta. This enables us to efficiently sample the energy landscape of the system using Gaussian accelerated MD. Key myosin loop residues, whose substitutions are related to cardiomyopathy, are identified to form stable or metastable interactions with the actin surface. We find that the actin-binding cleft closure is allosterically coupled to the myosin motor core transitions and ATP-hydrolysis product release from the active site. Furthermore, a gate between switch I and switch II is suggested to control phosphate release at the prepowerstroke state. Our approach demonstrates the ability to link sequence and structural information to motor functions.

RSAD2: An exclusive target protein for Zika virus comparative modeling, characterization, energy minimization and stabilization.

Objective: The major purpose of the present study was to predict the structure of Radical s-adenosyl-L-methionine Domain 2 (RSAD2), the most targeted protein of the Zika virus using comparative modeling, to validate the models that were generated and molecular dynamics (MD) simulations were performed. Methods: The secondary structure of RSAD2 was estimated using the Garnier-Osguthorpe-Robson, Self-Optimized Prediction method with Alignment, and Position-Specific Iterative-Blast based secondary structure prediction algorithms. The best of them were preferred based on their DOPE score, then three-dimensional structure identification using SWISS-MODEL and the Protein Homology/Analogy Recognition Engine (Phyre2) server. SAVES 6.0 was used to validate the models, and the preferred model was then energetically stabilized. The model with least energy minimization was used for MD simulations using iMODS. Results: The model predicted using SWISS-MODEL was determined as the best among the predicted models. In the Ramachandran plot, there were 238 residues (90.8%) in favored regions, 23 residues (8.8%) in allowed regions, and 1 residue (0.4%) in generously allowed regions. Energy minimization was calculated using Swiss PDB viewer, reporting the SWISS-MODEL with the lowest energy (E = -18439.475 KJ/mol) and it represented a stable structure conformation at three-dimensional level when analyzed by MD simulations. Conclusion: A large amount of sequence and structural data is now available, for tertiary protein structure prediction, hence implying a computational approach in all the aspects becomes an opportunistic strategy. The best three-dimensional structure of RSAD2 was built and was confirmed with energy minimization, secondary structure validation and torsional angles stabilization. This modeled protein is predicted to play a role in the development of drugs against Zika virus infection.

Identification of multi-targeting natural antiviral peptides to impede SARS-CoV-2 infection.

SARS-CoV-2 and its variants cause serious health concerns throughout the world. The alarming increase in the daily number of cases has become a nightmare in many low-income countries; although some vaccines are available, their high cost and low vaccine production make them unreachable to ordinary people in developing countries. Other treatment strategies are required for novel therapeutic options. The peptide-based drug is one of the alternatives with low toxicity, more specificity, and ease of synthesis. Herein, we have applied structure-based virtual screening to identify potential peptides targeting the critical proteins of SARS-CoV-2. Non-toxic natural antiviral peptides were selected from the enormous number of peptides. Comparative modeling was applied to prepare a 3D structure of selected peptides. 3D models of the peptides were docked using the ClusPro docking server to determine their binding affinity and peptide-protein interaction. The high-scoring peptides were docked with other crucial proteins to analyze multiple targeting peptides. The two best peptides were subjected to MD simulations to validate the structure stability and evaluated RMSD, RMSF, Rg, SASA, and H-bonding from the trajectory analysis of 100 ns. The proposed lead peptides can be used as a broad-spectrum drug and potentially develop as a therapeutic to combat SARS-CoV-2, positively impacting the current pandemic. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-022-02113-9.

Genome-Wide Characterization of Superoxide Dismutase (SOD) Genes in Daucus carota: Novel Insights Into Structure, Expression, and Binding Interaction With Hydrogen Peroxide (H2O2) Under Abiotic Stress Condition.

Superoxide dismutase (SOD) proteins are important antioxidant enzymes that help plants to grow, develop, and respond to a variety of abiotic stressors. SOD gene family has been identified in a number of plant species but not yet in Daucus carota. A total of 9 DcSOD genes, comprising 2 FeSODs, 2 MnSODs, and 5 Cu/ZnSODs, are identified in the complete genome of D. carota, which are dispersed in five out of nine chromosomes. Based on phylogenetic analysis, SOD proteins from D. carota were categorized into two main classes (Cu/ZnSODs and MnFeSODs). It was predicted that members of the same subgroups have the same subcellular location. The phylogenetic analysis was further validated by sequence motifs, exon-intron structure, and 3D protein structures, with each subgroup having a similar gene and protein structure. Cis-regulatory elements responsive to abiotic stresses were identified in the promoter region, which may contribute to their differential expression. Based on RNA-seq data, tissue-specific expression revealed that DcCSD2 had higher expression in both xylem and phloem. Moreover, DcCSD2 was differentially expressed in dark stress. All SOD genes were subjected to qPCR analysis after cold, heat, salt, or drought stress imposition. SODs are antioxidants and play a critical role in removing reactive oxygen species (ROS), including hydrogen peroxide (H2O2). DcSODs were docked with H2O2 to evaluate their binding. The findings of this study will serve as a basis for further functional insights into the DcSOD gene family.

Differences in Charge Distribution in Leishmania tarentolae Leishmanolysin Result in a Reduced Enzymatic Activity.

Leishmania tarentolae is a non-pathogenic trypanosomatid isolated from lizards widely used for heterologous protein expression and extensively studied to understand the pathogenic mechanisms of leishmaniasis. The repertoire of leishmanolysin genes was reported to be expanded in L. tarentolae genome, but no proteolytic activity was detected. Here, we analyzed L. tarentolae leishmanolysin proteins from the genome to the structural levels and evaluated the enzymatic activity of the wild-type and overexpressing mutants of leishmanolysin. A total of 61 leishmanolysin sequences were retrieved from the L. tarentolae genome. Five of them were selected for phylogenetic analysis, and for three of them, we built 3D models based on the crystallographic structure of L. major ortholog. Molecular dynamics simulations of these models disclosed a less negative electrostatic potential compared to the template. Subsequently, L. major LmjF.10.0460 and L. tarentolae LtaP10.0650 leishmanolysins were cloned in a pLEXSY expression system into L. tarentolae. Proteins from the wild-type and the overexpressing parasites were submitted to enzymatic analysis. Our results revealed that L. tarentolae leishmanolysins harbor a weak enzymatic activity about three times less abundant than L. major leishmanolysin. Our findings strongly suggest that the less negative electrostatic potential of L. tarentolae leishmanolysin can be the reason for the reduced proteolytic activity detected in this parasite.

Structural Comparative Modeling of Multi-Domain F508del CFTR.

Cystic fibrosis (CF) is a rare genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial anion channel expressed in several vital organs. Absence of functional CFTR results in imbalanced osmotic equilibrium and subsequent mucus build up in the lungs-which increases the risk of infection and eventually causes death. CFTR is an ATP-binding cassette (ABC) transporter family protein composed of two transmembrane domains (TMDs), two nucleotide binding domains (NBDs), and an unstructured regulatory domain. The most prevalent patient mutation is the deletion of F508 (F508del), making F508del CFTR the primary target for current FDA approved CF therapies. However, no experimental multi-domain F508del CFTR structure has been determined and few studies have modeled F508del using multi-domain WT CFTR structures. Here, we used cryo-EM density data and Rosetta comparative modeling (RosettaCM) to compare a F508del model with published experimental data on CFTR NBD1 thermodynamics. We then apply this modeling method to generate multi-domain WT and F508del CFTR structural models. These models demonstrate the destabilizing effects of F508del on NBD1 and the NBD1/TMD interface in both the inactive and active conformation of CFTR. Furthermore, we modeled F508del/R1070W and F508del bound to the CFTR corrector VX-809. Our models reveal the stabilizing effects of VX-809 on multi-domain models of F508del CFTR and pave the way for rational design of additional drugs that target F508del CFTR for treatment of CF.

Functional annotation and comparative modeling of ligninolytic enzymes from Trametes villosa (SW.) Kreisel for biotechnological applications.

Functional annotation of Trametes villosa genome was performed to search Class II peroxidase proteins in this white-rot fungus, which can be valuable for several biotechnological processes. After sequence identification and manual curation, five proteins were selected to build 3 D models by comparative modeling. Analysis of sequential and structural sequences from selected targets revealed the presence of two putative Lignin Peroxidase and three putative Manganese Peroxidase on this fungal genome. All 3 D models had a similar folding pattern from selected 3 D structure templates. After minimization and validation steps, the best 3 D models were subjected to docking studies and molecular dynamics to identify structural requirements and the interactions required for molecular recognition. Two reliable 3 D models of Class II peroxidases, with typical catalytic site and architecture, and its protein sequences are indicated to recombinant production in biotechnological applications, such as bioenergy.Communicated by Ramaswamy H. Sarma.

Proteomic Tools for the Analysis of Cytoskeleton Proteins.

Proteomic analyses have become an essential part of the toolkit of the molecular biologist, given the widespread availability of genomic data and open source or freely accessible bioinformatics software. Tools are available for detecting homologous sequences, recognizing functional domains, and modeling the three-dimensional structure for any given protein sequence, as well as for predicting interactions with other proteins or macromolecules. Although a wealth of structural and functional information is available for many cytoskeletal proteins, with representatives spanning all of the major subfamilies, the majority of cytoskeletal proteins remain partially or totally uncharacterized. Moreover, bioinformatics tools provide a means for studying the effects of synthetic mutations or naturally occurring variants of these cytoskeletal proteins. This chapter discusses various freely available proteomic analysis tools, with a focus on in silico prediction of protein structure and function. The selected tools are notable for providing an easily accessible interface for the novice while retaining advanced functionality for more experienced computational biologists.