DNA binding and bridging by human CtIP in the healthy and diseased states

The human DNA repair factor CtIP helps to initiate the resection of double-stranded DNA breaks for repair by homologous recombination, in part through its ability to bind and bridge DNA molecules. However, CtIP is a natively disordered protein that bears no apparent similarity to other DNA-binding proteins and so the structural basis for these activities remains unclear. In this work, we have used bulk DNA binding, single molecule tracking, and DNA bridging assays to study wild-type and variant CtIP proteins to better define the DNA binding domains and the effects of mutations associated with inherited human disease. Our work identifies a monomeric DNA-binding domain in the C-terminal region of CtIP. CtIP binds non-specifically to DNA and can diffuse over thousands of nucleotides. CtIP-mediated bridging of distant DNA segments is observed in single-molecule magnetic tweezers experiments. However, we show that binding alone is insufficient for DNA bridging, which also requires tetramerization via the N-terminal domain. Variant CtIP proteins associated with Seckel and Jawad syndromes display impaired DNA binding and bridging activities. The significance of these findings in the context of facilitating DNA break repair is discussed.

Purification, Characterization, cDNA Cloning, and Bioinformatic Analysis of Zinc-Binding Protein from Magallana hongkongensis

Oysters contain significant amounts of the zinc element, which may also be found in their proteins. In this study, a novel zinc-binding protein was purified from the mantle of the oyster Magallana hongkongensis using two kinds of gel filtration chromatograms. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) showed that its molecular weight was approximately 36 kDa. The protein identified by the Q-Exactive mass spectrometer shared the highest sequence identity with carbonic anhydrase derived from Crassostrea gigas concerning amino acid sequence similarity. Based on homologous cloning and RACE PCR, the full-length cDNA of carbonic anhydrase from Magallana hongkongensis (designated as MhCA) was cloned and sequenced. The cDNA of MhCA encodes a 315-amino-acid protein with 89.74% homology to carbonic anhydrase derived from Crassostrea gigas. Molecular docking revealed that the two zinc ions primarily form coordination bonds with histidine residues in the MhCA protein. These results strongly suggest that MhCA is a novel zinc-binding protein in Magallana hongkongensis.

Mycobacterial resistance to zinc poisoning requires assembly of P-ATPase-containing membrane metal efflux platforms

The human pathogen Mycobacterium tuberculosis requires a P_1B-ATPase metal exporter, CtpC (Rv3270), for resistance to zinc poisoning. Here, we show that zinc resistance also depends on a chaperone-like protein, PacL1 (Rv3269). PacL1 contains a transmembrane domain, a cytoplasmic region with glutamine/alanine repeats and a C-terminal metal-binding motif (MBM). PacL1 binds Zn²⁺, but the MBM is required only at high zinc concentrations. PacL1 co-localizes with CtpC in dynamic foci in the mycobacterial plasma membrane, and the two proteins form high molecular weight complexes. Foci formation does not require flotillin nor the PacL1 MBM. However, deletion of the PacL1 Glu/Ala repeats leads to loss of CtpC and sensitivity to zinc. Genes pacL1 and ctpC appear to be in the same operon, and homologous gene pairs are found in the genomes of other bacteria. Furthermore, PacL1 colocalizes and functions redundantly with other PacL orthologs in M. tuberculosis. Overall, our results indicate that PacL proteins may act as scaffolds that assemble P-ATPase-containing metal efflux platforms mediating bacterial resistance to metal poisoning.

The human pathogen Mycobacterium tuberculosis requires a metal exporter, CtpC, for resistance to zinc poisoning. Here, the authors show that zinc resistance also depends on a chaperone-like protein that binds zinc ions, forms high-molecular-weight complexes with CtpC in the cytoplasmic membrane, and is required for CtpC function.

GraphSite: Ligand Binding Site Classification with Deep Graph Learning

The binding of small organic molecules to protein targets is fundamental to a wide array of cellular functions. It is also routinely exploited to develop new therapeutic strategies against a variety of diseases. On that account, the ability to effectively detect and classify ligand binding sites in proteins is of paramount importance to modern structure-based drug discovery. These complex and non-trivial tasks require sophisticated algorithms from the field of artificial intelligence to achieve a high prediction accuracy. In this communication, we describe GraphSite, a deep learning-based method utilizing a graph representation of local protein structures and a state-of-the-art graph neural network to classify ligand binding sites. Using neural weighted message passing layers to effectively capture the structural, physicochemical, and evolutionary characteristics of binding pockets mitigates model overfitting and improves the classification accuracy. Indeed, comprehensive cross-validation benchmarks against a large dataset of binding pockets belonging to 14 diverse functional classes demonstrate that GraphSite yields the class-weighted F1-score of 81.7%, outperforming other approaches such as molecular docking and binding site matching. Further, it also generalizes well to unseen data with the F1-score of 70.7%, which is the expected performance in real-world applications. We also discuss new directions to improve and extend GraphSite in the future.

Current understanding of the interactions between metal ions and Apolipoprotein E in Alzheimer's disease

Alzheimer's disease (AD), the most common type of dementia in the elderly, is a chronic and progressive neurodegenerative disorder with no effective disease-modifying treatments to date. Studies have shown that an imbalance in brain metal ions, such as zinc, copper, and iron, is closely related to the onset and progression of AD. Many efforts have been made to understand metal-related mechanisms and therapeutic strategies for AD. Emerging evidence suggests that interactions of brain metal ions and apolipoprotein E (ApoE), which is the strongest genetic risk factor for late-onset AD, may be one of the mechanisms for neurodegeneration. Here, we summarize the key points regarding how metal ions and ApoE contribute to the pathogenesis of AD. We further describe the interactions between metal ions and ApoE in the brain and propose that their interactions play an important role in neuropathological alterations and cognitive decline in AD.

A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level

Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein-metal ion interactions. Compared to biophysical or biochemical wet-lab technologies, computational methods provide open web interfaces of high-resolution databases and high-throughput predictors for efficient investigation of metal-binding residues. This review surveys and details 18 public databases of metal-protein binding. We collect a comprehensive set of 44 computation-based methods and classify them into four categories, namely, learning-, docking-, template-, and meta-based methods. We analyze the benchmark datasets, assessment criteria, feature construction, and algorithms. We also compare several methods on two benchmark testing datasets and include a discussion about currently publicly available predictive tools. Finally, we summarize the challenges and underlying limitations of the current studies and propose several prospective directions concerning the future development of the related databases and methods.

The Hippo pathway kinases LATS1 and LATS2 attenuate cellular responses to heavy metals through phosphorylating MTF1

Heavy metals are both integral parts of cells and environmental toxicants, and their deregulation is associated with severe cellular dysfunction and various diseases. Here we show that the Hippo pathway plays a critical role in regulating heavy metal homeostasis. Hippo signalling deficiency promotes the transcription of heavy metal response genes and protects cells from heavy metal-induced toxicity, a process independent of its classic downstream effectors YAP and TAZ. Mechanistically, the Hippo pathway kinase LATS phosphorylates and inhibits MTF1, an essential transcription factor in the heavy metal response, resulting in the loss of heavy metal response gene transcription and cellular protection. Moreover, LATS activity is inhibited following heavy metal treatment, where accumulated zinc directly binds and inhibits LATS. Together, our study reveals an interplay between the Hippo pathway and heavy metals, providing insights into this growth-related pathway in tissue homeostasis and stress response.

Protein embeddings and deep learning predict binding residues for various ligand classes

One important aspect of protein function is the binding of proteins to ligands, including small molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of experimental progress many binding sites remain obscure. Here, we proposed bindEmbed21, a method predicting whether a protein residue binds to metal ions, nucleic acids, or small molecules. The Artificial Intelligence (AI)-based method exclusively uses embeddings from the Transformer-based protein Language Model (pLM) ProtT5 as input. Using only single sequences without creating multiple sequence alignments (MSAs), bindEmbed21DL outperformed MSA-based predictions. Combination with homology-based inference increased performance to F1 = 48 ± 3% (95% CI) and MCC = 0.46 ± 0.04 when merging all three ligand classes into one. All results were confirmed by three independent data sets. Focusing on very reliably predicted residues could complement experimental evidence: For the 25% most strongly predicted binding residues, at least 73% were correctly predicted even when ignoring the problem of missing experimental annotations. The new method bindEmbed21 is fast, simple, and broadly applicable-neither using structure nor MSAs. Thereby, it found binding residues in over 42% of all human proteins not otherwise implied in binding and predicted about 6% of all residues as binding to metal ions, nucleic acids, or small molecules.

Purification, Identification, and Characterization of a Glycoside Hydrolase Family 11-Xylanase with High Activity from Aspergillus niger VTCC 017

Xylanases (EC 3.2.1.8) have been considered as a potential green solution for the sustainable development of a wide range of industries including pulp and paper, food and beverages, animal feed, pharmaceuticals, and biofuels because they are the key enzymes that degrade the xylosidic linkages of xylan, the major component of the second most abundant raw material worldwide. Therefore, there is a critical need for the industrialized xylanases which must have high specific activity, be tolerant to organic solvent or detergent and be active during a wide range of conditions, such as high temperature and pH. In this study, an extracellular xylanase was purified from the culture broth of Aspergillus niger VTCC 017 for primary structure determination and properties characterization. The successive steps of purification comprised centrifugation, Sephadex G-100 filtration, and DEAE-Sephadex chromatography. The purified xylanase (specific activity reached 6596.79 UI/mg protein) was a monomer with a molecular weight of 37 kDa estimating from SDS electrophoresis. The results of LC/MS suggested that the purified protein is indeed an endo-1,4-β-D-xylanase. The purified xylanase showed the optimal temperature of 55 °C, and pH 6.5 with a stable xylanolytic activity within the temperature range of 45-50 °C, and within the pH range of 5.0-8.0. Most divalent metal cations including Zn²⁺, Fe²⁺, Mg²⁺, Cu²⁺, Mn²⁺ showed some inhibition of xylanase activity while the monovalent metal cations such as K⁺ and Ag⁺ exhibited slight stimulating effects on the enzyme activity. The introduction of 10-30% different organic solvents (n-butanol, acetone, isopropanol) and several detergents (Triton X-100, Tween 20, and SDS) slightly reduced the enzyme activity. Moreover, the purified xylanase seemed to be tolerant to methanol and ethanol and was even stimulated by Tween 80. Overall, with these distinctive properties, the putative xylanase could be a successful candidate for numerous industrial uses.

Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy

The 2-oxoglutarate/Fe (II)-dependent (2OG) oxygenase superfamily is mainly responsible for protein modification, nucleic acid repair and/or modification, and fatty acid metabolism and plays important roles in cancer, cardiovascular disease, and other diseases. They are likely to become new targets for the treatment of cancer and other diseases, so the accurate identification of 2OG oxygenases is of great significance. Many computational methods have been proposed to predict functional proteins to compensate for the time-consuming and expensive experimental identification. However, machine learning has not been applied to the study of 2OG oxygenases. In this study, we developed OGFE_RAAC, a prediction model to identify whether a protein is a 2OG oxygenase. To improve the performance of OGFE_RAAC, 673 amino acid reduction alphabets were used to determine the optimal feature representation scheme by recoding the protein sequence. The 10-fold cross-validation test showed that the accuracy of the model in identifying 2OG oxygenases is 91.04%. Besides, the independent dataset results also proved that the model has excellent generalization and robustness. It is expected to become an effective tool for the identification of 2OG oxygenases. With further research, we have also found that the function of 2OG oxygenases may be related to their polarity and hydrophobicity, which will help the follow-up study on the catalytic mechanism of 2OG oxygenases and the way they interact with the substrate. Based on the model we built, a user-friendly web server was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ogferaac.

Inferring metal binding sites in flexible regions of proteins

Metal ions are central to the molecular function of many proteins. Thus their knowledge in experimentally determined structure is important; however, such structures often lose bound metal ions during sample preparation. Identification of these metal-binding site(s) becomes difficult when the receptor is novel and/or their conformations differ in the bound/unbound states. Locating such sites in theoretical models also poses a challenge due to the uncertainties with side-chain modeling. We address the problem by employing the Geometric Hashing algorithm to create a template library of functionally important binding sites and match query structures with the available templates. The matching is done on the structure ensemble obtained from coarse-grained molecular dynamics simulation, where metal-specific amino acids are screened to infer the true site. Test on 1347 non-redundant monomer protein structures show that Ca²⁺ , Zn²⁺ , Mg²⁺ , Cu²⁺ , and Fe³⁺ binding site residues can be classified at 0.92, 0.95, 0.80, 0.90, and 0.92 aggregate performance (out of 1) across all possible thresholds. The performance for Ca²⁺ and Zn²⁺ is notably superior in comparison to state-of-the-art methods like IonCom and MIB. Specific case studies show that additionally predicted metal-binding site residues in proteins have features necessary for ion binding. These include new sites not predicted by other methods. The use of coarse-grained dynamics thus provides a generalized approach to improve metal-binding site prediction. The work is expected to contribute to improving our ability to correctly predict protein molecular function where knowledge of metal binding is a key requirement.

A structure-function study of ZraP and ZraS provides new insights into the two-component system Zra

BACKGROUND

Zra belongs to the envelope stress response (ESR) two-component systems (TCS). It is atypical because of its third periplasmic repressor partner (ZraP), in addition to its histidine kinase sensor protein (ZraS) and its response regulator (ZraR) components. Furthermore, although it is activated by Zn²⁺, it is not involved in zinc homeostasis or protection against zinc toxicity. Here, we mainly focus on ZraS but also provide information on ZraP.

METHODS

The purified periplasmic domain of ZraS and ZraP were characterized using biophysical and biochemical technics: multi-angle laser light scattering (MALLS), circular dichroism (CD), differential scanning fluorescence (DSF), inductively coupled plasma atomic emission spectroscopy (ICP-AES), cross-linking and small-angle X-ray scattering (SAXS). In-vivo experiments were carried out to determine the redox state of the cysteine residue in ZraP and the consequences for the cell of an over-activation of the Zra system.

RESULTS

We show that ZraS binds one Zn²⁺ molecule with high affinity resulting in conformational changes of the periplasmic domain, consistent with a triggering function of the metal ion. We also demonstrate that, in the periplasm, the only cysteine residue of ZraP is at least partially reduced. Using SAXS, we conclude that the previously determined X-ray structure is different from the structure in solution.

CONCLUSION

Our results allow us to propose a general mechanism for the Zra system activation and to compare it to the homologous Cpx system.

GENERAL SIGNIFICANCE

We bring new input on the so far poorly described Zra system and notably on ZraS.

THE USE OF MASS SPECTROMETRY TO STUDY ZN-METALLOPROTEASE-SUBSTRATE INTERACTIONS

Zinc metalloproteases (ZnMPs) participate in diverse biological reactions, encompassing the synthesis and degradation of all the major metabolites in living organisms. In particular, ZnMPs have been recognized to play a very important role in controlling the concentration level of several peptides and/or proteins whose homeostasis has to be finely regulated for the correct physiology of cells. Dyshomeostasis of aggregation-prone proteins causes pathological conditions and the development of several different diseases. For this reason, in recent years, many analytical approaches have been applied for studying the interaction between ZnMPs and their substrates and how environmental factors can affect enzyme activities. In this scenario, mass spectrometric methods occupy a very important role in elucidating different aspects of ZnMPs-substrates interaction. These range from identification of cleavage sites to quantitation of kinetic parameters. In this work, an overview of all the main achievements regarding the application of mass spectrometric methods to investigating ZnMPs-substrates interactions is presented. A general experimental protocol is also described which may prove useful to the study of similar interactions. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Boosting Granular Support Vector Machines for the Accurate Prediction of Protein-Nucleotide Binding Sites

AIM AND OBJECTIVE

The accurate identification of protein-ligand binding sites helps elucidate protein function and facilitate the design of new drugs. Machine-learning-based methods have been widely used for the prediction of protein-ligand binding sites. Nevertheless, the severe class imbalance phenomenon, where the number of nonbinding (majority) residues is far greater than that of binding (minority) residues, has a negative impact on the performance of such machine-learning-based predictors.

MATERIALS AND METHODS

In this study, we aim to relieve the negative impact of class imbalance by Boosting Multiple Granular Support Vector Machines (BGSVM). In BGSVM, each base SVM is trained on a granular training subset consisting of all minority samples and some reasonably selected majority samples. The efficacy of BGSVM for dealing with class imbalance was validated by benchmarking it with several typical imbalance learning algorithms. We further implemented a protein-nucleotide binding site predictor, called BGSVM-NUC, with the BGSVM algorithm.

RESULTS

Rigorous cross-validation and independent validation tests for five types of proteinnucleotide interactions demonstrated that the proposed BGSVM-NUC achieves promising prediction performance and outperforms several popular sequence-based protein-nucleotide binding site predictors. The BGSVM-NUC web server is freely available at http://csbio.njust.edu.cn/bioinf/BGSVM-NUC/ for academic use.

Bioinformatics of Metalloproteins and Metalloproteomes

Trace metals are inorganic elements that are required for all organisms in very low quantities. They serve as cofactors and activators of metalloproteins involved in a variety of key cellular processes. While substantial effort has been made in experimental characterization of metalloproteins and their functions, the application of bioinformatics in the research of metalloproteins and metalloproteomes is still limited. In the last few years, computational prediction and comparative genomics of metalloprotein genes have arisen, which provide significant insights into their distribution, function, and evolution in nature. This review aims to offer an overview of recent advances in bioinformatic analysis of metalloproteins, mainly focusing on metalloprotein prediction and the use of different metals across the tree of life. We describe current computational approaches for the identification of metalloprotein genes and metal-binding sites/patterns in proteins, and then introduce a set of related databases. Furthermore, we discuss the latest research progress in comparative genomics of several important metals in both prokaryotes and eukaryotes, which demonstrates divergent and dynamic evolutionary patterns of different metalloprotein families and metalloproteomes. Overall, bioinformatic studies of metalloproteins provide a foundation for systematic understanding of trace metal utilization in all three domains of life.

GM-DockZn: a geometry matching-based docking algorithm for zinc proteins

Motivation

Molecular docking is a widely used technique for large-scale virtual screening of the interactions between small-molecule ligands and their target proteins. However, docking methods often perform poorly for metalloproteins due to additional complexity from the three-way interactions among amino-acid residues, metal ions and ligands. This is a significant problem because zinc proteins alone comprise about 10% of all available protein structures in the protein databank. Here, we developed GM-DockZn that is dedicated for ligand docking to zinc proteins. Unlike the existing docking methods developed specifically for zinc proteins, GM-DockZn samples ligand conformations directly using a geometric grid around the ideal zinc-coordination positions of seven discovered coordination motifs, which were found from the survey of known zinc proteins complexed with a single ligand.

Results

GM-DockZn has the best performance in sampling near-native poses with correct coordination atoms and numbers within the top 50 and top 10 predictions when compared to several state-of-the-art techniques. This is true not only for a non-redundant dataset of zinc proteins but also for a homolog set of different ligand and zinc-coordination systems for the same zinc proteins. Similar superior performance of GM-DockZn for near-native-pose sampling was also observed for docking to apo-structures and cross-docking between different ligand complex structures of the same protein. The highest success rate for sampling nearest near-native poses within top 5 and top 1 was achieved by combining GM-DockZn for conformational sampling with GOLD for ranking. The proposed geometry-based sampling technique will be useful for ligand docking to other metalloproteins.

Availability and implementation

GM-DockZn is freely available at www.qmclab.com/ for academic users.

Supplementary information

Supplementary data are available at Bioinformatics online.

Associative Interactions among Zinc, Apolipoprotein E, and Amyloid-β in the Amyloid Pathology

Zinc and apolipoprotein E (apoE) are reportedly involved in the pathology of Alzheimer's disease. To investigate the associative interaction among zinc, apoE, and amyloid-β (Aβ) and its role in amyloid pathogenesis, we performed various biochemical and immunoreactive analyses using brain tissues of Tg2576 mice and synthetic Aβ and apoE peptides. On amyloid plaques or in brain lysates of Tg2576 mice, apoE and Aβ immunoreactivities increased after zinc chelation and were restored by its subsequent replacement. Zinc depletion dissociated apoE/Aβ complexes or larger-molecular sizes of Aβ oligomers/aggregates into smaller-molecular sizes of apoE and/or Aβ monomers/complexes. In the presence of zinc, synthetic apoE and/or Aβ peptides aggregated into larger-molecular sizes of oligomers or complexes. Endogenous proteases or plasmin in brain lysates degraded apoE and/or Aβ complexes, and their proteolytic activity increased with zinc depletion. These biochemical findings suggest that zinc associates with apoE and Aβ to encourage the formation of apoE/Aβ complexes or large aggregates, raising the deposition of zinc-rich amyloid plaques. In turn, the presence of abundant zinc around and within apoE/Aβ complexes may block the access or activity of Aβ-degrading antibodies or proteases. These results support the plausibility of chelation strategy aiming at reducing amyloid pathology in Alzheimer's disease.

Divalent Cations and the Divergence of βγ-Crystallin Function

The βγ-crystallin superfamily contains both β- and γ-crystallins of the vertebrate eye lens and the microbial calcium-binding proteins, all of which are characterized by a common double-Greek key domain structure. The vertebrate βγ-crystallins are long-lived structural proteins that refract light onto the retina. In contrast, the microbial βγ-crystallins bind calcium ions. The βγ-crystallin from the tunicate Ciona intestinalis (Ci-βγ) provides a potential link between these two functions. It binds calcium with high affinity and is found in a light-sensitive sensory organ that is highly enriched in metal ions. Thus, Ci-βγ is valuable for investigating the evolution of the βγ-crystallin fold away from calcium binding and toward stability in the apo form as part of the vertebrate lens. Here, we investigate the effect of Ca²⁺ and other divalent cations on the stability and aggregation propensity of Ci-βγ and human γS-crystallin (HγS). Beyond Ca²⁺, Ci-βγ is capable of coordinating Mg²⁺, Sr²⁺, Co²⁺, Mn²⁺, Ni²⁺, and Zn²⁺, although only Sr²⁺ is bound with comparable affinity to its preferred metal ion. The extent to which the tested divalent cations stabilize Ci-βγ structure correlates strongly with ionic radius. In contrast, none of the tested divalent cations improved the stability of HγS, and some of them induced aggregation. Zn²⁺, Ni²⁺, and Co²⁺ induce aggregation by interacting with cysteine residues, whereas Cu²⁺-mediated aggregation proceeds via a different binding site.

Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods

The zinc (Zn²⁺) cofactor has been proven to be involved in numerous biological mechanisms and the zinc-binding site is recognized as one of the most important post-translation modifications in proteins. Therefore, accurate knowledge of zinc ions in protein structures can provide potential clues for elucidation of protein folding and functions. However, determining zinc-binding residues by experimental means is usually lab-intensive and associated with high cost in most cases. In this context, the development of computational tools for identifying zinc-binding sites is highly desired, especially in the current post-genomic era. In this work, we developed a novel zinc-binding site prediction method by combining several intensively-trained machine learning models. To establish an accurate and generative method, we downloaded all zinc-binding proteins from the Protein Data Bank and prepared a non-redundant dataset. Meanwhile, a well-prepared dataset by other groups was also used. Then, effective and complementary features were extracted from sequences and three-dimensional structures of these proteins. Moreover, several well-designed machine learning models were intensively trained to construct accurate models. To assess the performance, the obtained predictors were stringently benchmarked using the diverse zinc-binding sites. Furthermore, several state-of-the-art in silico methods developed specifically for zinc-binding sites were also evaluated and compared. The results confirmed that our method is very competitive in real world applications and could become a complementary tool to wet lab experiments. To facilitate research in the community, a web server and stand-alone program implementing our method were constructed and are publicly available at . The downloadable program of our method can be easily used for the high-throughput screening of potential zinc-binding sites across proteomes.

Prediction of Protein Metal Binding Sites Using Deep Neural Networks

Metals have crucial roles for many physiological, pathological and diagnostic processes. Metal binding proteins or metalloproteins are important for metabolism functions. The proteins that reach the three-dimensional structure by folding show which vital function is fulfilled. The prediction of metal-binding in proteins will be considered as a step-in function assignment for new proteins, which helps to obtain functional proteins in genomic studies, is critical to protein function annotation and drug discovery. Computational predictions made by using machine learning methods from the data obtained from amino acid sequences are widely used in the protein metal-binding and various bioinformatics fields. In this work, we present three different deep learning architectures for prediction of metal-binding of Histidines (HIS) and Cysteines (CYS) amino acids. These architectures are as follows: 2D Convolutional Neural Network, Long-Short Term Memory and Recurrent Neural Network. Their comparison is carried out on the three different sets of attributes derived from a public dataset of protein sequences. These three sets of features extracted from the protein sequence were obtained using the PAM scoring matrix, protein composition server, and binary representation methods. The results show that a better performance for prediction of protein metal- binding sites is obtained through Convolutional Neural Network architecture.

Trace Elements and Healthcare: A Bioinformatics Perspective

Biological trace elements are essential for human health. Imbalance in trace element metabolism and homeostasis may play an important role in a variety of diseases and disorders. While the majority of previous researches focused on experimental verification of genes involved in trace element metabolism and those encoding trace element-dependent proteins, bioinformatics study on trace elements is relatively rare and still at the starting stage. This chapter offers an overview of recent progress in bioinformatics analyses of trace element utilization, metabolism, and function, especially comparative genomics of several important metals. The relationship between individual elements and several diseases based on recent large-scale systematic studies such as genome-wide association studies and case-control studies is discussed. Lastly, developments of ionomics and its recent application in human health are also introduced.

Prediction of zinc binding sites in proteins using sequence derived information

Zinc is one the most abundant catalytic cofactor and also an important structural component of a large number of metallo-proteins. Hence prediction of zinc metal binding sites in proteins can be a significant step in annotation of molecular function of a large number of proteins. Majority of existing methods for zinc-binding site predictions are based on a data-set of proteins, which has been compiled nearly a decade ago. Hence there is a need to develop zinc-binding site prediction system using the current updated data to include recently added proteins. Herein, we propose a support vector machine-based method, named as ZincBinder, for prediction of zinc metal-binding site in a protein using sequence profile information. The predictor was trained using fivefold cross validation approach and achieved 85.37% sensitivity with 86.20% specificity during training. Benchmarking on an independent non-redundant data-set, which was not used during training, showed better performance of ZincBinder vis-à-vis existing methods. Executable versions, source code, sample datasets, and usage instructions are available at http://proteininformatics.org/mkumar/znbinder/.

Protein ligand-specific binding residue predictions by an ensemble classifier

BACKGROUND

Prediction of ligand binding sites is important to elucidate protein functions and is helpful for drug design. Although much progress has been made, many challenges still need to be addressed. Prediction methods need to be carefully developed to account for chemical and structural differences between ligands.

RESULTS

In this study, we present ligand-specific methods to predict the binding sites of protein-ligand interactions. First, a sequence-based method is proposed that only extracts features from protein sequence information, including evolutionary conservation scores and predicted structure properties. An improved AdaBoost algorithm is applied to address the serious imbalance problem between the binding and non-binding residues. Then, a combined method is proposed that combines the current template-free method and four other well-established template-based methods. The above two methods predict the ligand binding sites along the sequences using a ligand-specific strategy that contains metal ions, acid radical ions, nucleotides and ferroheme. Testing on a well-established dataset showed that the proposed sequence-based method outperformed the profile-based method by 4-19% in terms of the Matthews correlation coefficient on different ligands. The combined method outperformed each of the individual methods, with an improvement in the average Matthews correlation coefficients of 5.55% over all ligands. The results also show that the ligand-specific methods significantly outperform the general-purpose methods, which confirms the necessity of developing elaborate ligand-specific methods for ligand binding site prediction.

CONCLUSIONS

Two efficient ligand-specific binding site predictors are presented. The standalone package is freely available for academic usage at http://dase.ecnu.edu.cn/qwdong/TargetCom/TargetCom_standalone.tar.gz  or request upon the corresponding author.

Molecular speciation and tissue compartmentation of zinc in durum wheat grains with contrasting nutritional status

Low concentration of zinc (Zn) in the endosperm of cereals is a major factor contributing to Zn deficiency in human populations. We have investigated how combined Zn and nitrogen (N) fertilization affects the speciation and localization of Zn in durum wheat (Triticum durum). Zn-binding proteins were analysed with liquid chromatography ICP-MS and Orbitrap MS(2) , respectively. Laser ablation ICP-MS with simultaneous Zn, sulphur (S) and phosphorus (P) detection was used for bioimaging of Zn and its potential ligands. Increasing the Zn and N supply had a major impact on the Zn concentration in the endosperm, reaching concentrations higher than current breeding targets. The S concentration also increased, but S was only partly co-localized with Zn. The mutual Zn and S enrichment was reflected in substantially more Zn bound to small cysteine-rich proteins (apparent size 10-30 kDa), whereas the response of larger proteins (apparent size > 50 kDa) was only modest. Most of the Zn-responsive proteins were associated with redox- and stress-related processes. This study offers a methodological platform to deepen the understanding of processes behind endosperm Zn enrichment. Novel information is provided on how the localization and speciation of Zn is modified during Zn biofortification of grains.

A Sequence-Based Dynamic Ensemble Learning System for Protein Ligand-Binding Site Prediction

BACKGROUND

Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands is important for drug design and protein docking studies. Most of the successful protein-ligand binding predictions were based on known structures. However, structural information is not largely available in practice due to the huge gap between the number of known protein sequences and that of experimentally solved structures.

RESULTS

This paper proposes a dynamic ensemble approach to identify protein-ligand binding residues by using sequence information only. To avoid problems resulting from highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we constructed several balanced data sets and we trained a random forest classifier for each of them. We dynamically selected a subset of classifiers according to the similarity between the target protein and the proteins in the training data set. The combination of the predictions of the classifier subset to each query protein target yielded the final predictions. The ensemble of these classifiers formed a sequence-based predictor to identify protein-ligand binding sites.

CONCLUSIONS

Experimental results on two Critical Assessment of protein Structure Prediction datasets and the ccPDB dataset demonstrated that of our proposed method compared favorably with the state-of-the-art.

AVAILABILITY

http://www2.ahu.edu.cn/pchen/web/LigandDSES.htm.

Potential DNA binding and nuclease functions of ComEC domains characterized in silico

Bacterial competence, which can be natural or induced, allows the uptake of exogenous double stranded DNA (dsDNA) into a competent bacterium. This process is known as transformation. A multiprotein assembly binds and processes the dsDNA to import one strand and degrade another yet the underlying molecular mechanisms are relatively poorly understood. Here distant relationships of domains in Competence protein EC (ComEC) of Bacillus subtilis (Uniprot: P39695) were characterized. DNA-protein interactions were investigated in silico by analyzing models for structural conservation, surface electrostatics and structure-based DNA binding propensity; and by data-driven macromolecular docking of DNA to models. Our findings suggest that the DUF4131 domain contains a cryptic DNA-binding OB fold domain and that the β-lactamase-like domain is the hitherto cryptic competence nuclease. Proteins 2016; 84:1431-1442. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Minimal Functional Sites in Metalloproteins and Their Usage in Structural Bioinformatics

Metal ions play a functional role in numerous biochemical processes and cellular pathways. Indeed, about 40% of all enzymes of known 3D structure require a metal ion to be able to perform catalysis. The interactions of the metals with the macromolecular framework determine their chemical properties and reactivity. The relevant interactions involve both the coordination sphere of the metal ion and the more distant interactions of the so-called second sphere, i.e., the non-bonded interactions between the macromolecule and the residues coordinating the metal (metal ligands). The metal ligands and the residues in their close spatial proximity define what we call a minimal functional site (MFS). MFSs can be automatically extracted from the 3D structures of metal-binding biological macromolecules deposited in the Protein Data Bank (PDB). They are 3D templates that describe the local environment around a metal ion or metal cofactor and do not depend on the overall macromolecular structure. MFSs provide a different view on metal-binding proteins and nucleic acids, completely focused on the metal. Here we present different protocols and tools based upon the concept of MFS to obtain deeper insight into the structural and functional properties of metal-binding macromolecules. We also show that structure conservation of MFSs in metalloproteins relates to local sequence similarity more strongly than to overall protein similarity.

Automatic generation of bioinformatics tools for predicting protein-ligand binding sites

MOTIVATION

Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful.

RESULTS

We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands.

AVAILABILITY AND IMPLEMENTATION

The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux.

CONTACT

shimizu@bi.a.u-tokyo.ac.jp

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Interaction of α-crystallin with some small molecules and its effect on its structure and function

BACKGROUND

α-Crystallin acts like a molecular chaperone by interacting with its substrate proteins and thus prevents their aggregation. It also interacts with various kinds of small molecules that affect its structure and function.

SCOPE OF REVIEW

In this article we will present a review of work done with respect to the interaction of ATP, peptide generated from lens crystallin and other proteins and some bivalent metal ions with α-crystallin and discuss the role of these interactions on its structure and function and cataract formation. We will also discuss the interaction of some hydrophobic fluorescence probes and surface active agents with α-crystallin.

MAJOR CONCLUSIONS

Small molecule interaction controls the structure and function of α-crystallin. ATP and Zn+2 stabilize its structure and enhance chaperone function. Therefore the depletion of these small molecules can be detrimental to maintenance of lens transparency. However, the accumulation of small peptides due to protease activity in the lens can also be harmful as the interaction of these peptides with α-crystallin and other crystallin proteins in the lens promotes aggregation and loss of lens transparency. The use of hydrophobic probe has led to a wealth of information regarding the location of substrate binding site and nature of chaperone-substrate interaction. Interaction of surface active agents with α-crystallin has helped us to understand the structural stability and oligomeric dissociation in α-crystallin.

GENERAL SIGNIFICANCE

These interactions are very helpful in understanding the mechanistic details of the structural changes and chaperone function of α-crystallin. This article is part of a Special Issue entitled Crystallin Biochemistry in Health and Disease.

LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone

Background

Protein-ligand binding is important for some proteins to perform their functions. Protein-ligand binding sites are the residues of proteins that physically bind to ligands. Despite of the recent advances in computational prediction for protein-ligand binding sites, the state-of-the-art methods search for similar, known structures of the query and predict the binding sites based on the solved structures. However, such structural information is not commonly available.

Results

In this paper, we propose a sequence-based approach to identify protein-ligand binding residues. We propose a combination technique to reduce the effects of different sliding residue windows in the process of encoding input feature vectors. Moreover, due to the highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we construct several balanced data sets, for each of which a random forest (RF)-based classifier is trained. The ensemble of these RF classifiers forms a sequence-based protein-ligand binding site predictor.

Conclusions

Experimental results on CASP9 and CASP8 data sets demonstrate that our method compares favorably with the state-of-the-art protein-ligand binding site prediction methods.

An integrative computational model for large-scale identification of metalloproteins in microbial genomes: a focus on iron-sulfur cluster proteins

Metalloproteins represent a ubiquitous group of molecules which are crucial to the survival of all living organisms. While several metal-binding motifs have been defined, it remains challenging to confidently identify metalloproteins from primary protein sequences using computational approaches alone. Here, we describe a comprehensive strategy based on a machine learning approach to design and assess a penalized generalized linear model. We used this strategy to detect members of the iron-sulfur cluster protein family. A new category of descriptors, whose profile is based on profile hidden Markov models, encoding structural information was combined with public descriptors into a linear model. The model was trained and tested on distinct datasets composed of well-characterized iron-sulfur protein sequences, and the resulting model provided higher sensitivity compared to a motif-based approach, while maintaining a good level of specificity. Analysis of this linear model allows us to detect and quantify the contribution of each descriptor, providing us with a better understanding of this complex protein family along with valuable indications for further experimental characterization. Two newly-identified proteins, YhcC and YdiJ, were functionally validated as genuine iron-sulfur proteins, confirming the prediction. The computational model was then applied to over 550 prokaryotic genomes to screen for iron-sulfur proteomes; the results are publicly available at: . This study represents a proof-of-concept for the application of a penalized linear model to identify metalloprotein superfamilies on a large-scale. The application employed here, screening for iron-sulfur proteomes, provides new candidates for further biochemical and structural analysis as well as new resources for an extensive exploration of iron-sulfuromes in the microbial world.

MetalS(3), a database-mining tool for the identification of structurally similar metal sites

We have developed a database search tool to identify metal sites having structural similarity to a query metal site structure within the MetalPDB database of minimal functional sites (MFSs) contained in metal-binding biological macromolecules. MFSs describe the local environment around the metal(s) independently of the larger context of the macromolecular structure. Such a local environment has a determinant role in tuning the chemical reactivity of the metal, ultimately contributing to the functional properties of the whole system. The database search tool, which we called MetalS(3) (Metal Sites Similarity Search), can be accessed through a Web interface at http://metalweb.cerm.unifi.it/tools/metals3/ . MetalS(3) uses a suitably adapted version of an algorithm that we previously developed to systematically compare the structure of the query metal site with each MFS in MetalPDB. For each MFS, the best superposition is kept. All these superpositions are then ranked according to the MetalS(3) scoring function and are presented to the user in tabular form. The user can interact with the output Web page to visualize the structural alignment or the sequence alignment derived from it. Options to filter the results are available. Test calculations show that the MetalS(3) output correlates well with expectations from protein homology considerations. Furthermore, we describe some usage scenarios that highlight the usefulness of MetalS(3) to obtain mechanistic and functional hints regardless of homology.

A nondiscriminating glutamyl-tRNA synthetase in the plasmodium apicoplast: the first enzyme in an indirect aminoacylation pathway

The malaria parasite Plasmodium falciparum and related organisms possess a relict plastid known as the apicoplast. Apicoplast protein synthesis is a validated drug target in malaria because antibiotics that inhibit translation in prokaryotes also inhibit apicoplast protein synthesis and are sometimes used for malaria prophylaxis or treatment. We identified components of an indirect aminoacylation pathway for Gln-tRNA(Gln) biosynthesis in Plasmodium that we hypothesized would be essential for apicoplast protein synthesis. Here, we report our characterization of the first enzyme in this pathway, the apicoplast glutamyl-tRNA synthetase (GluRS). We expressed the recombinant P. falciparum enzyme in Escherichia coli, showed that it is nondiscriminating because it glutamylates both apicoplast tRNA(Glu) and tRNA(Gln), determined its kinetic parameters, and demonstrated its inhibition by a known bacterial GluRS inhibitor. We also localized the Plasmodium berghei ortholog to the apicoplast in blood stage parasites but could not delete the PbGluRS gene. These data show that Gln-tRNA(Gln) biosynthesis in the Plasmodium apicoplast proceeds via an essential indirect aminoacylation pathway that is reminiscent of bacteria and plastids.

MetalS2: a tool for the structural alignment of minimal functional sites in metal-binding proteins and nucleic acids

We developed a new software tool, MetalS(2), for the structural alignment of Minimal Functional Sites (MFSs) in metal-binding biological macromolecules. MFSs are 3D templates that describe the local environment around the metal(s) independently of the larger context of the macromolecular structure. Such local environment has a determinant role in tuning the chemical reactivity of the metal, ultimately contributing to the functional properties of the whole system. On our example data sets, MetalS(2) unveiled structural similarities that other programs for protein structure comparison do not consistently point out and overall identified a larger number of structurally similar MFSs. MetalS(2) supports the comparison of MFSs harboring different metals and/or with different nuclearity and is available both as a stand-alone program and a Web tool ( http://metalweb.cerm.unifi.it/tools/metals2/).

Computationally characterizing and comprehensive analysis of zinc-binding sites in proteins

Zinc is one of the most essential metals utilized by organisms, and zinc-binding proteins play an important role in a variety of biological processes such as transcription regulation, cell metabolism and apoptosis. Thus, characterizing the precise zinc-binding sites is fundamental to an elucidation of the biological functions and molecular mechanisms of zinc-binding proteins. Using systematic analyses of structural characteristics, we observed that 4-residue and 3-residue zinc-binding sites have distinctly specific geometric features. Based on the results, we developed the novel computational program Geometric REstriction for Zinc-binding (GRE4Zn) to characterize the zinc-binding sites in protein structures, by restricting the distances between zinc and its coordinating atoms. The comparison between GRE4Zn and analogous tools revealed that it achieved a superior performance. A large-scale prediction for structurally characterized proteins was performed with this powerful predictor, and statistical analyses for the results indicated zinc-binding proteins have come to be significantly involved in more complicated biological processes in higher species than simpler species during the course of evolution. Further analyses suggested that zinc-binding proteins are preferentially implicated in a variety of diseases and highly enriched in known drug targets, and the prediction of zinc-binding sites can be helpful for the investigation of molecular mechanisms. In this regard, these prediction and analysis results should prove to be highly useful be helpful for further biomedical study and drug design. The online service of GRE4Zn is freely available at: http://biocomp.ustc.edu.cn/gre4zn/. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

MOTIF DISCOVERY WITH DATA MINING IN 3D PROTEIN STRUCTURE DATABASES: DISCOVERY, VALIDATION AND PREDICTION OF THE U-SHAPE ZINC BINDING ("HUF-ZINC") MOTIF

Data mining in protein databases, derivatives from more fundamental protein 3D structure and sequence databases, has considerable unearthed potential for the discovery of sequence motif—structural motif—function relationships as the finding of the U-shape (Huf-Zinc) motif, originally a small student's project, exemplifies. The metal ion zinc is critically involved in universal biological processes, ranging from protein-DNA complexes and transcription regulation to enzymatic catalysis and metabolic pathways. Proteins have evolved a series of motifs to specifically recognize and bind zinc ions. Many of these, so called zinc fingers, are structurally independent globular domains with discontinuous binding motifs made up of residues mostly far apart in sequence. Through a systematic approach starting from the BRIX structure fragment database, we discovered that there exists another predictable subset of zinc-binding motifs that not only have a conserved continuous sequence pattern but also share a characteristic local conformation, despite being included in totally different overall folds. While this does not allow general prediction of all Zn binding motifs, a HMM-based web server, Huf-Zinc, is available for prediction of these novel, as well as conventional, zinc finger motifs in protein sequences. The Huf-Zinc webserver can be freely accessed through this URL ( http://mendel.bii.a-star.edu.sg/METHODS/hufzinc/ ).

PreDNA: accurate prediction of DNA-binding sites in proteins by integrating sequence and geometric structure information

MOTIVATION

Protein-DNA interactions often take part in various crucial processes, which are essential for cellular function. The identification of DNA-binding sites in proteins is important for understanding the molecular mechanisms of protein-DNA interaction. Thus, we have developed an improved method to predict DNA-binding sites by integrating structural alignment algorithm and support vector machine-based methods.

RESULTS

Evaluated on a new non-redundant protein set with 224 chains, the method has 80.7% sensitivity and 82.9% specificity in the 5-fold cross-validation test. In addition, it predicts DNA-binding sites with 85.1% sensitivity and 85.3% specificity when tested on a dataset with 62 protein-DNA complexes. Compared with a recently published method, BindN+, our method predicts DNA-binding sites with a 7% better area under the receiver operating characteristic curve value when tested on the same dataset. Many important problems in cell biology require the dense non-linear interactions between functional modules be considered. Thus, our prediction method will be useful in detecting such complex interactions.

Prediction of Apoptosis Proteins Subcellular Location Using Evolutionary Profiles and Motifs Information

Apoptosis proteins are very important for regulating the balance between cell proliferation and death. Because the function of apoptosis protein is closely related to its subcellular location, it is desirable to explore their function by predicting the subcellular location of apoptosis protein. In this paper, based on evolutionary profiles and motifs information of protein sequences, an approach for predicting apoptosis proteins subcellular location is presented by using support vector machine (SVM). When the method is applied to three data sets (98 apoptosis proteins dataset, 225 apoptosis proteins dataset and 317 apoptosis proteins dataset), the overall accuracies of our method on the three data sets reach 95.9%, 86.7% and 91.8% in the jackknife test, respectively. The higher predictive success rates indicate that the proposed method is very useful for apoptosis proteins subcellular localization.

An integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteins

Zinc-binding proteins are the most abundant metalloproteins in the Protein Data Bank where the zinc ions usually have catalytic, regulatory or structural roles critical for the function of the protein. Accurate prediction of zinc-binding sites is not only useful for the inference of protein function but also important for the prediction of 3D structure. Here, we present a new integrative framework that combines multiple sequence and structural properties and graph-theoretic network features, followed by an efficient feature selection to improve prediction of zinc-binding sites. We investigate what information can be retrieved from the sequence, structure and network levels that is relevant to zinc-binding site prediction. We perform a two-step feature selection using random forest to remove redundant features and quantify the relative importance of the retrieved features. Benchmarking on a high-quality structural dataset containing 1,103 protein chains and 484 zinc-binding residues, our method achieved >80% recall at a precision of 75% for the zinc-binding residues Cys, His, Glu and Asp on 5-fold cross-validation tests, which is a 10%-28% higher recall at the 75% equal precision compared to SitePredict and zincfinder at residue level using the same dataset. The independent test also indicates that our method has achieved recall of 0.790 and 0.759 at residue and protein levels, respectively, which is a performance better than the other two methods. Moreover, AUC (the Area Under the Curve) and AURPC (the Area Under the Recall-Precision Curve) by our method are also respectively better than those of the other two methods. Our method can not only be applied to large-scale identification of zinc-binding sites when structural information of the target is available, but also give valuable insights into important features arising from different levels that collectively characterize the zinc-binding sites. The scripts and datasets are available at http://protein.cau.edu.cn/zincidentifier/.

Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure

RNA-protein interactions play important roles in various biological processes. The precise detection of RNA-protein interaction sites is very important for understanding essential biological processes and annotating the function of the proteins. In this study, based on various features from amino acid sequence and structure, including evolutionary information, solvent accessible surface area and torsion angles (φ, ψ) in the backbone structure of the polypeptide chain, a computational method for predicting RNA-binding sites in proteins is proposed. When the method is applied to predict RNA-binding sites in three datasets: RBP86 containing 86 protein chains, RBP107 containing 107 proteins chains and RBP109 containing 109 proteins chains, better sensitivities and specificities are obtained compared to previously published methods in five-fold cross-validation tests. In order to make further examination for the efficiency of our method, the RBP107 dataset is used as training set, RBP86 and RBP109 datasets are used as the independent test sets. In addition, as examples of our prediction, RNA-binding sites in a few proteins are presented. The annotated results are consistent with the PDB annotation. These results show that our method is useful for annotating RNA binding sites of novel proteins.

George M, Freedman H, Barakat KH, Damaraju S, Hameroff S, Tuszynski JA. Computational predictions of volatile anesthetic interactions with the microtubule cytoskeleton: implications for side effects of general anesthesia

The cytoskeleton is essential to cell morphology, cargo trafficking, and cell division. As the neuronal cytoskeleton is extremely complex, it is no wonder that a startling number of neurodegenerative disorders (including but not limited to Alzheimer's disease, Parkinson's disease and Huntington's disease) share the common feature of a dysfunctional neuronal cytoskeleton. Recently, concern has been raised about a possible link between anesthesia, post-operative cognitive dysfunction, and the exacerbation of neurodegenerative disorders. Experimental investigations suggest that anesthetics bind to and affect cytoskeletal microtubules, and that anesthesia-related cognitive dysfunction involves microtubule instability, hyper-phosphorylation of the microtubule-associated protein tau, and tau separation from microtubules. However, exact mechanisms are yet to be identified. In this paper the interaction of anesthetics with the microtubule subunit protein tubulin is investigated using computer-modeling methods. Homology modeling, molecular dynamics simulations and surface geometry techniques were used to determine putative binding sites for volatile anesthetics on tubulin. This was followed by free energy based docking calculations for halothane (2-bromo-2-chloro-1,1,1-trifluoroethane) on the tubulin body, and C-terminal regions for specific tubulin isotypes. Locations of the putative binding sites, halothane binding energies and the relation to cytoskeleton function are reported in this paper.