Fast computation, rotation, and comparison of low resolution spherical harmonic molecular surfaces

Greener Strategy for Lupanine Purification from Lupin Bean Wastewaters Using a Molecularly Imprinted Polymer

Lupanine is an alkaloid used in the pharma industry as a building block or precursor in the synthesis of sparteine and also explored for drug synthesis in the pharma industry as a chiral selector. This alkaloid is found in lupin bean processing wastewaters originated from the debittering process to make these beans edible. In this work, a computational chemistry approach was taken to design molecularly imprinted polymers (MIPs) selecting itaconic acid, a biobased building block, as a functional monomer that can provide higher affinities for lupanine. MIP-1 was prepared using lupanine as the template, itaconic acid as a functional monomer, and ethylene glycol dimethacrylate as a cross-linker by bulk polymerization. Lupanine was concentrated from lupin bean wastewater by nanofiltration, extracted with ethyl acetate, and purified using the synthesized MIP. MIP-1 was able to selectively recognize lupanine and improve the purity of lupanine from 78 to 88%, with 82% recovery of the alkaloid. These results show the potential application of this strategy to render the industrial process more sustainable.

Model-based plant phenomics on morphological traits using morphometric descriptors

The morphological traits of plants contribute to many important functional features such as radiation interception, lodging tolerance, gas exchange efficiency, spatial competition between individuals and/or species, and disease resistance. Although the importance of plant phenotyping techniques is increasing with advances in molecular breeding strategies, there are barriers to its advancement, including the gap between measured data and phenotypic values, low quantitativity, and low throughput caused by the lack of models for representing morphological traits. In this review, we introduce morphological descriptors that can be used for phenotyping plant morphological traits. Geometric morphometric approaches pave the way to a general-purpose method applicable to single units. Hierarchical structures composed of an indefinite number of multiple elements, which is often observed in plants, can be quantified in terms of their multi-scale topological characteristics using topological data analysis. Theoretical morphological models capture specific anatomical structures, if recognized. These morphological descriptors provide us with the advantages of model-based plant phenotyping, including robust quantification of limited datasets. Moreover, we discuss the future possibilities that a system of model-based measurement and model refinement would solve the lack of morphological models and the difficulties in scaling out the phenotyping processes.

Protein sequence-to-structure learning: Is this the end(-to-end revolution)?

The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy. This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. Novel emerging approaches include (i) geometric learning, that is, learning on representations such as graphs, three-dimensional (3D) Voronoi tessellations, and point clouds; (ii) pretrained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; and (vi) finally truly end-to-end architectures, that is, differentiable models starting from a sequence and returning a 3D structure. Here, we provide an overview and our opinion of the novel deep learning approaches developed in the last 2 years and widely used in CASP14.

Parametric-based feature selection via spherical harmonic coefficients for the left ventricle myocardial infarction screening

Computer-aided diagnosis (CAD) of heart diseases using machine learning techniques has recently received much attention. In this study, we present a novel parametric-based feature selection method using the three-dimensional spherical harmonic (SHs) shape descriptors of the left ventricle (LV) for intelligent myocardial infarction (MI) classification. The main hypothesis is that the SH coefficients of the parameterized endocardial shapes in MI patients are recognizable and distinguishable from healthy subjects. The SH parameterization, expansion, and registration of the LV endocardial shapes were performed, then parametric-based features were extracted. The proposed method performance was investigated by varying considered phases (i.e., the end-systole (ES) or the end-diastole (ED) frames), the spatial alignment procedures based on three modes (i.e., the center of the apical (CoA), the center of mass (CoM), and the center of the basal (CoB)), and considered orders of SH coefficients. After applying principal component analysis (PCA) on the feature vectors, support vector machine (SVM), K-nearest neighbors (K-NN), and random forest (RF) were trained and tested using the leave-one-out cross-validation (LOOCV). The proposed method validation was performed via a dataset containing healthy and MI subjects selected from the automated cardiac diagnosis challenge (ACDC) database. The promising results show the effectiveness of the proposed classification model. SVM reached the best performance with accuracy, sensitivity, specificity, and F-score of 97.50%, 95.00%, 100.00%, and 97.56%, respectively, using the introduced optimum feature set. This study demonstrates the robustness of combining the SH coefficients and machine learning techniques. We also quantify and notably highlight the contribution of different parameters in the classification and finally introduce an optimal feature set with maximum discriminant strength for the MI classification task. Moreover, the obtained results confirm that the proposed method performs more accurately than conventional point-based methods and also the current start-of-the-art, i.e., clinical measures. We showed our method's generalizability using employing it in dilated cardiomyopathy (DCM) detection and achieving promising results too. Parametric-based feature selection via spherical harmonics coefficients for the left ventricle myocardial infarction screening.

Evaluation of the antiglycating potential of thymoquinone and its interaction with BSA

Thymoquinone (TQ) is a bioactive component of medicinal plant, Nigella sativa. It has been identified as promising anti-inflammatory and anti-analgesic properties. In the present study, the TQ has been investigated for physiological interaction as well as binding properties with serum albumin and their thermodynamic parameters at different temperatures. Glycation process was checked with the measurement of fructosamine content, carbonyl content and total advanced glycated end products. The aggregation of amyloid β-structure was measured with Thioflavin-T and the secondary structure of BSA was observed by circular dichroism (CD) in glycated and thermal treated samples. The results indicate that the TQ showed binding interaction (both static and dynamic) with BSA (K_b= 18.31 × 10⁷ M^-1 at 293 K) and suppression of glycated products. The glycation-induced and thermal aggregation were prevented and the secondary structure of BSA was maintained. Therefore, these findings suggest that TQ may be used for a therapeutic drug for antiglycation as well as anti-aggregation.Communicated by Ramaswamy H. Sarma.

Synthesis, molecular modelling, FT-IR, Raman and NMR characterization, molecular docking and ADMET study of new nickel(II) complex with an N4-tetradentate thiosemicarbazone

A new nickel(II) complex was synthesized by using S-propyl-thiosemicarbazide and 2-amino-3,5-dibromobenzaldehyde. The complex, obtained by the template effect of nickel ions, was structurally analysed by experimental and theoretical vibrational spectroscopy, NMR and density functional theory (DFT) calculations. By using DFT/B3LYP method with 6-311++G(d, p) basis set, the most stable molecular structure of the title molecule was calculated. The fundamental vibrational wavenumbers, IR and Raman intensities for the optimized structure of the molecule under investigation were determined and compared with the experimental vibrational spectra. The vibrational assignment was achieved using the calculated potential energy distributions of the vibrational modes. Moreover, the molecular electrostatic potential (MEP), the highest occupied molecular orbital (HOMO) and the lowest occupied molecular orbital (LUMO) energies were calculated, Molecular docking of the molecule was carried out against DNA in order to identify the potential inhibitory action of the title compound. The findings suggested that the aforementioned compound has a strong binding affinity to interact with DNA residues DT8, DC9, DG12, DG16, DA17, and DA18 through the intermolecular hydrogen bonds. Also the performed in silico ADMET analysis was the prediction of the synthesized molecule's pharmacokinetic and toxicity profile expressing good oral drug like actions and non-toxic nature. The complex has been shown to have the possibility to become a model molecule for drug development processes.Communicated by Ramaswamy H. Sarma.

PatchSearch: a web server for off-target protein identification

The large number of proteins found in the human body implies that a drug may interact with many proteins, called off-target proteins, besides its intended target. The PatchSearch web server provides an automated workflow that allows users to identify structurally conserved binding sites at the protein surfaces in a set of user-supplied protein structures. Thus, this web server may help to detect potential off-target protein. It takes as input a protein complexed with a ligand and identifies within user-defined or predefined collections of protein structures, those having a binding site compatible with this ligand in terms of geometry and physicochemical properties. It is based on a non-sequential local alignment of the patch over the entire protein surface. Then the PatchSearch web server proposes a ligand binding mode for the potential off-target, as well as an estimated affinity calculated by the Vinardo scoring function. This novel tool is able to efficiently detects potential interactions of ligands with distant off-target proteins. Furthermore, by facilitating the discovery of unexpected off-targets, PatchSearch could contribute to the repurposing of existing drugs. The server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PatchSearch.

Multiepitope Subunit Vaccine to Evoke Immune Response against Acute Encephalitis

Acute encephalitis syndrome outbreak has emerged as a major health concern on both national and international scales. Brain inflammation/infections caused by Japanese encephalitis virus (JEV) can lead to death. The cases are growing in numbers globally, and this emergent health concern requires an effective and viable vaccine to strengthen the body's immune system against this deadly virus. Proteomic analyses of JEV revealed the envelope protein as a potential target for vaccine development by patient samples analysis. Hence, in this study, we aimed to design a multiepitope subunit vaccine for acute encephalitis using the advanced structural biology and immunoinformatics approaches. We report the multiepitope subunit vaccine consisted of the putative T-cell epitope (MHC-1 and MHC-2 restricted) and B-cell epitope and with high antigenicity and immunogenicity. The TAP affinity epitopes along with adjuvants were engineered to the vaccine, to ensure the ease transportation inside the host and elicitation of a strong immune response. The specificity of vaccine construct was evaluated by molecular docking with major histocompatibility complex (MHC) receptors and host membrane receptor TLR2. High docking scores and a close interaction to the binding groove of receptors confirmed the potency and specificity of the vaccine. Also, molecular dynamics simulation studies confirmed the stable interaction of vaccine with TLR2 for a long run (100 ns), which showed the prolonged elicitation of the strong immune response. Peptide dynamics studies showed the flexible, strong, and stable binding of vaccine with minimal deviation in root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and secondary structure estimation (SSE) plots till 100 ns simulation run. The in silico immune simulation approach based on the position-specific scoring matrix and machine learning methods resulted in the strong immune response reinforcement statistics of immune cells (T-cells, B-cells population, and memory cells) in response to vaccine candidate. The favorable results and well-correlated data of varied in silico techniques paved for a potent multiepitope vaccine and helped us to propose the mechanism of action of designed vaccine and generation of the immune response against acute encephalitis syndrome.

Comparative analysis of the shape and size of the middle ear cavity of turtles reveals no correlation with habitat ecology

The middle ear of turtles differs from other reptiles in being separated into two distinct compartments. Several ideas have been proposed as to why the middle ear is compartmentalized in turtles, most suggesting a relationship with underwater hearing. Extant turtle species span fully marine to strictly terrestrial habitats, and ecomorphological hypotheses of turtle hearing predict that this should correlate with variation in the structure of the middle ear due to differences in the fluid properties of water and air. We investigate the shape and size of the air-filled middle ear cavity of 56 extant turtles using 3D data and phylogenetic comparative analysis to test for correlations between habitat preferences and the shape and size of the middle ear cavity. Only weak correlations are found between middle ear cavity size and ecology, with aquatic taxa having proportionally smaller cavity volumes. The middle ear cavity of turtles exhibits high shape diversity among species, but we found no relationship between this shape variation and ecology. Surprisingly, the estimated acoustic transformer ratio, a key functional parameter of impedance-matching ears in vertebrates, also shows no relation to habitat preferences (aquatic/terrestrial) in turtles. We suggest that middle ear cavity shape may be controlled by factors unrelated to hearing, such as the spatial demands of surrounding cranial structures. A review of the fossil record suggests that the modern turtle ear evolved during the Early to Middle Jurassic in stem turtles broadly adapted to freshwater and terrestrial settings. This, combined with our finding that evolutionary transitions between habitats caused only weak evolutionary changes in middle ear structure, suggests that tympanic hearing in turtles evolved as a compromise between subaerial and underwater hearing.

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.

Similarity Search of Flexible 3D Molecules Combining Local and Global Shape Descriptors

In this paper, a framework for shape-based similarity search of 3D molecular structures is presented. The proposed framework exploits simultaneously the discriminative capabilities of a global, a local, and a hybrid local-global shape feature to produce a geometric descriptor that achieves higher retrieval accuracy than each feature does separately. Global and hybrid features are extracted using pairwise computations of diffusion distances between the points of the molecular surface, while the local feature is based on accumulating pairwise relations among oriented surface points into local histograms. The local features are integrated into a global descriptor vector using the bag-of-features approach. Due to the intrinsic property of its constituting shape features to be invariant to articulations of the 3D objects, the framework is appropriate for similarity search of flexible 3D molecules, while at the same time it is also accurate in retrieving rigid 3D molecules. The proposed framework is evaluated in flexible and rigid shape matching of 3D protein structures as well as in shape-based virtual screening of large ligand databases with quite promising results.

An experimental and quantum chemical study on the non-covalent interactions of a cyclometallated Rh(iii) complex with DNA and BSA

The interaction of a new cyclometallated Rh(iii) complex with DNA and BSA was investigated. The three-layer ONIOM method was employed to calculate the interaction energy between DNA and the complex.

Diffusion MRI abnormalities detection with orientation distribution functions: a multiple sclerosis longitudinal study

We propose a new algorithm for the voxelwise analysis of orientation distribution functions between one image and a group of reference images. It relies on a generic framework for the comparison of diffusion probabilities on the sphere, sampled from the underlying models. We demonstrate that this method, combined to dimensionality reduction through a principal component analysis, allows for more robust detection of lesions on simulated data when compared to classical tensor-based analysis. We then demonstrate the efficiency of this pipeline on the longitudinal comparison of multiple sclerosis patients at an early stage of the disease: right after their first clinically isolated syndrome (CIS) and three months later. We demonstrate the predictive value of ODF-based scores for the early detection of lesions that will appear or heal.

A new scoring function for protein-protein docking that identifies native structures with unprecedented accuracy

Protein-protein (P-P) 3D structures are fundamental to structural biology and drug discovery. However, most of them have never been determined. Many docking algorithms were developed for that purpose, but they have a very limited accuracy in generating native-like structures and identifying the most correct one, in particular when a single answer is asked for. With such a low success rate it is difficult to point out one docked structure as being native-like. Here we present a new, high accuracy, scoring method to identify the 3D structure of P-P complexes among a set of trial poses. It incorporates alanine scanning mutagenesis experimental data that need to be obtained a priori. The scoring scheme works by matching the computational and the experimental alanine scanning mutagenesis results. The size of the trial P-P interface area is also taken into account. We show that the method ranks the trial structures and identifies the native-like structures with unprecedented accuracy (∼94%), providing the correct P-P 3D structures that biochemists and molecular biologists need to pursue their studies. With such a success rate, the bottleneck of protein-protein docking moves from the scoring to searching algorithms.

Distinct molecular features facilitating ice-binding mechanisms in hyperactive antifreeze proteins closely related to an Antarctic sea ice bacterium

Antifreeze proteins or ice-binding proteins (IBPs) facilitate the survival of certain cellular organisms in freezing environment by inhibiting the growth of ice crystals in solution. Present study identifies orthologs of the IBP of Colwellia sp. SLW05, which were obtained from a wide range of taxa. Phylogenetic analysis on the basis of conserved regions (predicted as the 'ice-binding domain' [IBD]) present in all the orthologs, separates the bacterial and archaeal orthologs from that of the eukaryotes'. Correspondence analysis pointed out that the bacterial and archaeal IBDs have relatively higher average hydrophobicity than the eukaryotic members. IBDs belonging to bacterial as well as archaeal AFPs contain comparatively more strands, and therefore are revealed to be under higher evolutionary selection pressure. Molecular docking studies prove that the ice crystals form more stable complex with the bacterial as well as archaeal proteins than the eukaryotic orthologs. Analysis of the docked structures have traced out the ice-binding sites (IBSs) in all the orthologs which continue to facilitate ice-binding activity even after getting mutated with respect to the well-studied IBSs of Typhula ishikariensis and notably, all these mutations performing ice-binding using 'anchored clathrate mechanism' have been found to prefer polar and hydrophilic amino acids. Horizontal gene transfer studies point toward a strong selection pressure favoring independent evolution of the IBPs in some polar organisms including prokaryotes as well as eukaryotes because these proteins facilitate the polar organisms to acclimatize to the adversities in their niche, thus safeguarding their existence.

Structure of a bifunctional alcohol dehydrogenase involved in bioethanol generation inGeobacillus thermoglucosidasius

Bifunctional alcohol/aldehyde dehydrogenase (ADHE) enzymes are found within many fermentative microorganisms. They catalyse the conversion of an acyl-coenzyme A to an alcoholviaan aldehyde intermediate; this is coupled to the oxidation of two NADH molecules to maintain the NAD+pool during fermentative metabolism. The structure of the alcohol dehydrogenase (ADH) domain of an ADHE protein from the ethanol-producing thermophileGeobacillus thermoglucosidasiushas been determined to 2.5 Å resolution. This is the first structure to be reported for such a domain.In silicomodelling has been carried out to generate a homology model of the aldehyde dehydrogenase domain, and this was subsequently docked with the ADH-domain structure to model the structure of the complete ADHE protein. This model suggests, for the first time, a structural mechanism for the formation of the large multimeric assemblies or `spirosomes' that are observed for this ADHE protein and which have previously been reported for ADHEs from other organisms.

Isolating influential regions of electrostatic focusing in protein and DNA structure

Electrostatic focusing is a general phenomenon that occurs in cavities and grooves on the molecular surface of biomolecules. Narrow surface features can partially shield charged atoms from the high-dielectric solvent, enhancing electrostatic potentials inside the cavity and projecting electric field lines outward into the solvent. This effect has been observed in many instances and is widely considered in the human examination of molecular structure, but it is rarely integrated into the digital representations used in protein structure comparison software. To create a computational representation of electrostatic focusing, that is compatible with structure comparison algorithms, this paper presents an approach that generates three-dimensional solids that approximate regions where focusing occurs. We verify the accuracy of this representation against instances of focusing in proteins and DNA. Noting that this representation also identifies thin focusing regions on the molecular surface that are unlikely to affect binding, we describe a second algorithm that conservatively isolates larger focusing regions. The resulting 3D solids can be compared with Boolean set operations, permitting a new range of analyses on the regions where electrostatic focusing occurs. They also represent a novel integration of molecular shape and electrostatic focusing into the same structure comparison framework.

Integrative relational machine-learning for understanding drug side-effect profiles

Background

Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence.

Results

In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site.

Conclusions

Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs.

Mutations in the NOTCH pathway regulator MIB1 cause left ventricular noncompaction cardiomyopathy

Left ventricular noncompaction (LVNC) causes prominent ventricular trabeculations and reduces cardiac systolic function. The clinical presentation of LVNC ranges from asymptomatic to heart failure. We show that germline mutations in human MIB1 (mindbomb homolog 1), which encodes an E3 ubiquitin ligase that promotes endocytosis of the NOTCH ligands DELTA and JAGGED, cause LVNC in autosomal-dominant pedigrees, with affected individuals showing reduced NOTCH1 activity and reduced expression of target genes. Functional studies in cells and zebrafish embryos and in silico modeling indicate that MIB1 functions as a dimer, which is disrupted by the human mutations. Targeted inactivation of Mib1 in mouse myocardium causes LVNC, a phenotype mimicked by inactivation of myocardial Jagged1 or endocardial Notch1. Myocardial Mib1 mutants show reduced ventricular Notch1 activity, expansion of compact myocardium to proliferative, immature trabeculae and abnormal expression of cardiac development and disease genes. These results implicate NOTCH signaling in LVNC and indicate that MIB1 mutations arrest chamber myocardium development, preventing trabecular maturation and compaction.

A regionalizable statistical model of intersecting regions in protein-ligand binding cavities

Finding elements of proteins that influence ligand binding specificity is an essential aspect of research in many fields. To assist in this effort, this paper presents two statistical models, based on the same theoretical foundation, for evaluating structural similarity among binding cavities. The first model specializes in the "unified" comparison of whole cavities, enabling the selection of cavities that are too dissimilar to have similar binding specificity. The second model enables a "regionalized" comparison of cavities within a user-defined region, enabling the selection of cavities that are too dissimilar to bind the same molecular fragments in the given region. We applied these models to analyze the ligand binding cavities of the serine protease and enolase superfamilies. Next, we observed that our unified model correctly separated sets of cavities with identical binding preferences from other sets with varying binding preferences, and that our regionalized model correctly distinguished cavity regions that are too dissimilar to bind similar molecular fragments in the user-defined region. These observations point to applications of statistical modeling that can be used to examine and, more importantly, identify influential structural similarities within binding site structure in order to better detect influences on protein-ligand binding specificity.

Spherical Harmonics based extraction and annotation of cell shape in 3D time-lapse microscopy sequences

Understanding the mechanisms involved in cell deformation and motility is of major interest in numerous areas of life sciences. Precise quantification of cell shape requires robust shape description tools to be amenable to subsequent analysis and classification. The main difficulty lies in the great variability of cell shapes within a given homogeneous population. In this work, we propose a framework for cell shape extraction and classification for 3D time-lapse sequences of living cells, based on the SPherical HARMonics transform (SPHARM). Starting from an initial segmentation of the cell surface over time, this mathematical representation enables us to represent each extracted surface by a unique set of coefficients, while taking into account invariance properties such as translation or orientation. Then, unsupervised classification is conducted using a multi-class K-Means approach, so as to extract the most pertinent number of classes representing the different phases of the cell deformation. Experimental results on several sequences give encouraging results, and show that the proposed approach can be used to perform automated sequence annotation, and can be further applied to compare shape characteristics across different cell populations.

Modeling regionalized volumetric differences in protein-ligand binding cavities

Identifying elements of protein structures that create differences in protein-ligand binding specificity is an essential method for explaining the molecular mechanisms underlying preferential binding. In some cases, influential mechanisms can be visually identified by experts in structural biology, but subtler mechanisms, whose significance may only be apparent from the analysis of many structures, are harder to find. To assist this process, we present a geometric algorithm and two statistical models for identifying significant structural differences in protein-ligand binding cavities. We demonstrate these methods in an analysis of sequentially nonredundant structural representatives of the canonical serine proteases and the enolase superfamily. Here, we observed that statistically significant structural variations identified experimentally established determinants of specificity. We also observed that an analysis of individual regions inside cavities can reveal areas where small differences in shape can correspond to differences in specificity.

Structure- and sequence-based function prediction for non-homologous proteins

The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recent developments of local structure-based and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, protein function prediction and extended similarity group, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures.

A novel, customizable and optimizable parameter method using spherical harmonics for molecular shape similarity comparisons

A novel molecular shape similarity comparison method, namely SHeMS, derived from spherical harmonic (SH) expansion, is presented in this study. Through weight optimization using genetic algorithms for a customized reference set, the optimal combination of weights for the translationally and rotationally invariant (TRI) SH shape descriptor, which can specifically and effectively distinguish overall and detailed shape features according to the molecular surface, is obtained for each molecule. This method features two key aspects: firstly, the SH expansion coefficients from different bands are weighted to calculate similarity, leading to a distinct contribution of overall and detailed features to the final score, and thus can be better tailored for each specific system under consideration. Secondly, the reference set for optimization can be totally configured by the user, which produces great flexibility, allowing system-specific and customized comparisons. The directory of useful decoys (DUD) database was adopted to validate and test our method, and principal component analysis (PCA) reveals that SH descriptors for shape comparison preserve sufficient information to separate actives from decoys. The results of virtual screening indicate that the proposed method based on optimal SH descriptor weight combinations represents a great improvement in performance over original SH (OSH) and ultra-fast shape recognition (USR) methods, and is comparable to many other popular methods. Through combining efficient shape similarity comparison with SH expansion method, and other aspects such as chemical and pharmacophore features, SHeMS can play a significant role in this field and can be applied practically to virtual screening by means of similarity comparison with 3D shapes of known active compounds or the binding pockets of target proteins.

Spherical harmonics coefficients for ligand-based virtual screening of cyclooxygenase inhibitors

BACKGROUND

Molecular descriptors are essential for many applications in computational chemistry, such as ligand-based similarity searching. Spherical harmonics have previously been suggested as comprehensive descriptors of molecular structure and properties. We investigate a spherical harmonics descriptor for shape-based virtual screening.

METHODOLOGY/PRINCIPAL FINDINGS

We introduce and validate a partially rotation-invariant three-dimensional molecular shape descriptor based on the norm of spherical harmonics expansion coefficients. Using this molecular representation, we parameterize molecular surfaces, i.e., isosurfaces of spatial molecular property distributions. We validate the shape descriptor in a comprehensive retrospective virtual screening experiment. In a prospective study, we virtually screen a large compound library for cyclooxygenase inhibitors, using a self-organizing map as a pre-filter and the shape descriptor for candidate prioritization.

CONCLUSIONS/SIGNIFICANCE

12 compounds were tested in vitro for direct enzyme inhibition and in a whole blood assay. Active compounds containing a triazole scaffold were identified as direct cyclooxygenase-1 inhibitors. This outcome corroborates the usefulness of spherical harmonics for representation of molecular shape in virtual screening of large compound collections. The combination of pharmacophore and shape-based filtering of screening candidates proved to be a straightforward approach to finding novel bioactive chemotypes with minimal experimental effort.

Diffeomorphic image registration of diffusion MRI using spherical harmonics

Nonrigid registration of diffusion magnetic resonance imaging (MRI) is crucial for group analyses and building white matter and fiber tract atlases. Most current diffusion MRI registration techniques are limited to the alignment of diffusion tensor imaging (DTI) data. We propose a novel diffeomorphic registration method for high angular resolution diffusion images by mapping their orientation distribution functions (ODFs). ODFs can be reconstructed using q-ball imaging (QBI) techniques and represented by spherical harmonics (SHs) to resolve intra-voxel fiber crossings. The registration is based on optimizing a diffeomorphic demons cost function. Unlike scalar images, deforming ODF maps requires ODF reorientation to maintain its consistency with the local fiber orientations. Our method simultaneously reorients the ODFs by computing a Wigner rotation matrix at each voxel, and applies it to the SH coefficients during registration. Rotation of the coefficients avoids the estimation of principal directions, which has no analytical solution and is time consuming. The proposed method was validated on both simulated and real data sets with various metrics, which include the distance between the estimated and simulated transformation fields, the standard deviation of the general fractional anisotropy and the directional consistency of the deformed and reference images. The registration performance using SHs with different maximum orders were compared using these metrics. Results show that the diffeomorphic registration improved the affine alignment, and registration using SHs with higher order SHs further improved the registration accuracy by reducing the shape difference and improving the directional consistency of the registered and reference ODF maps.

Interaction site prediction by structural similarity to neighboring clusters in protein-protein interaction networks

BACKGROUND

Recently, revealing the function of proteins with protein-protein interaction (PPI) networks is regarded as one of important issues in bioinformatics. With the development of experimental methods such as the yeast two-hybrid method, the data of protein interaction have been increasing extremely. Many databases dealing with these data comprehensively have been constructed and applied to analyzing PPI networks. However, few research on prediction interaction sites using both PPI networks and the 3D protein structures complementarily has explored.

RESULTS

We propose a method of predicting interaction sites in proteins with unknown function by using both of PPI networks and protein structures. For a protein with unknown function as a target, several clusters are extracted from the neighboring proteins based on their structural similarity. Then, interaction sites are predicted by extracting similar sites from the group of a protein cluster and the target protein. Moreover, the proposed method can improve the prediction accuracy by introducing repetitive prediction process.

CONCLUSIONS

The proposed method has been applied to small scale dataset, then the effectiveness of the method has been confirmed. The challenge will now be to apply the method to large-scale datasets.

F2Dock: fast Fourier protein-protein docking

The functions of proteins are often realized through their mutual interactions. Determining a relative transformation for a pair of proteins and their conformations which form a stable complex, reproducible in nature, is known as docking. It is an important step in drug design, structure determination, and understanding function and structure relationships. In this paper, we extend our nonuniform fast Fourier transform-based docking algorithm to include an adaptive search phase (both translational and rotational) and thereby speed up its execution. We have also implemented a multithreaded version of the adaptive docking algorithm for even faster execution on multicore machines. We call this protein-protein docking code F2Dock (F2 = Fast Fourier). We have calibrated F2Dock based on an extensive experimental study on a list of benchmark complexes and conclude that F2Dock works very well in practice. Though all docking results reported in this paper use shape complementarity and Coulombic-potential-based scores only, F2Dock is structured to incorporate Lennard-Jones potential and reranking docking solutions based on desolvation energy .

Internal Dynamics of a Coarse-Grained Protein Using Analytical Harmonic Representation

An analytical coarse-grained model (ACG) is introduced to represent individual macromolecules for simulation of dynamic processes in cells. In the ACG model, a macromolecular structure is treated as a fully coarse-grained entity with a uniform mass density without the explicit atomic details. The excluded volume and surface of the ACG macromolecular species are explicitly treated by a spherical harmonic representation in the present study (although ellipsoidal, solid, and radial augmented functions can be used), which can provide any desired accuracy and detail depending on the problem of interest. The present paper focuses on the description of the internal fluctuations of a single ACG macromolecule, modeled by the superposition of low frequency quasiharmonic modes from explicit molecular dynamics simulation. A procedure for estimating the amplitudes, time scales of the quasiharmonic motions and the corresponding phases is presented and used to synthesize the complex motion. The analytical description and numerical algorithm can provide an adequate representation of the internal protein fluctuations revealed from the corresponding atomistic simulations, although the internal motions of ACG macromolecules do not explore motions not exhibited in the dynamic simulations.

Species recognition and patterns of population variation in the reproductive structures of a damselfly genus

The selection pressures imposed by mate choice for species identity should impose strong stabilizing selection on traits that confer species identity to mates. Thus, we expect that such traits should show nonoverlapping distributions among closely related species, but show little to no variance among populations within a species. We tested these predictions by comparing levels of population differentiation in the sizes and shapes of male cerci (i.e., the clasper structures used for species identity during mating) of six Enallagma damselfly species. Cerci shapes were nonoverlapping among Enallagma species, and five of six Enallagma species showed no population variation across their entire species ranges. In contrast, cerci sizes overlapped among species and varied substantially among populations within species. These results, taken with previous studies, suggest that cerci shape is a primary feature used in species recognition used to discriminate conspecific from heterospecifics during mating.

Binding balls: fast detection of binding sites using a property of spherical Fourier transform

The functional prediction of proteins is one of the most challenging problems in modern biology. An established computational technique involves the identification of three-dimensional local similarities in proteins. In this article, we present a novel method to quickly identify promising binding sites. Our aim is to efficiently detect putative binding sites without explicitly aligning them. Using the theory of Spherical Harmonics, a candidate binding site is modeled as a Binding Ball. The Binding Ball signature, offered by the Spherical Fourier coefficients, can be efficiently used for a fast detection of putative regions. Our contribution includes the Binding Ball modeling and the definition of a scoring function that does not require aligning candidate regions. Our scoring function can be computed efficiently using a property of Spherical Fourier transform (SFT) that avoids the evaluation of all alignments. Experiments on different ligands show good discrimination power when searching for known binding sites. Moreover, we prove that this method can save up to 40% in time compared with traditional approaches.

Application of 3D Zernike descriptors to shape-based ligand similarity searching

BACKGROUND

The identification of promising drug leads from a large database of compounds is an important step in the preliminary stages of drug design. Although shape is known to play a key role in the molecular recognition process, its application to virtual screening poses significant hurdles both in terms of the encoding scheme and speed.

RESULTS

In this study, we have examined the efficacy of the alignment independent three-dimensional Zernike descriptor (3DZD) for fast shape based similarity searching. Performance of this approach was compared with several other methods including the statistical moments based ultrafast shape recognition scheme (USR) and SIMCOMP, a graph matching algorithm that compares atom environments. Three benchmark datasets are used to thoroughly test the methods in terms of their ability for molecular classification, retrieval rate, and performance under the situation that simulates actual virtual screening tasks over a large pharmaceutical database. The 3DZD performed better than or comparable to the other methods examined, depending on the datasets and evaluation metrics used. Reasons for the success and the failure of the shape based methods for specific cases are investigated. Based on the results for the three datasets, general conclusions are drawn with regard to their efficiency and applicability.

CONCLUSION

The 3DZD has unique ability for fast comparison of three-dimensional shape of compounds. Examples analyzed illustrate the advantages and the room for improvements for the 3DZD.

Rearrangements in the relative orientation of cytoplasmic domains induced by a membrane-anchored protein mediate modulations in Kv channel gating

Interdomain interactions between intracellular N and C termini have been described for various K(+) channels, including the voltage-gated Kv2.1, and suggested to affect channel gating. However, no channel regulatory protein directly affecting N/C interactions has been demonstrated. Most Kv2.1 channel interactions with regulatory factors occur at its C terminus. The vesicular SNARE that is also present at a high concentration in the neuronal plasma membrane, VAMP2, is the only protein documented to affect Kv2.1 gating by binding to its N terminus. As its binding target has been mapped near a site implicated in Kv2.1 N/C interactions, we hypothesized that VAMP2 binding to the N terminus requires concomitant conformational changes in the C terminus, which wraps around the N terminus from the outside, to give VAMP2 access. Here, we first determined that the Kv2.1 N terminus, although crucial, is not sufficient to convey functional interaction with VAMP2, and that, concomitant to its binding to the "docking loop" at the Kv2.1 N terminus, VAMP2 binds to the proximal part of the Kv2.1 C terminus, C1a. Next, using computational biology approaches (ab initio modeling, docking, and molecular dynamics simulations) supported by molecular biology, biochemical, electrophysiological, and fluorescence resonance energy transfer analyses, we mapped the interaction sites on both VAMP2 and Kv2.1 and found that this interaction is accompanied by rearrangements in the relative orientation of Kv2.1 cytoplasmic domains. We propose that VAMP2 modulates Kv2.1 inactivation by interfering with the interaction between the docking loop and C1a, a mechanism for gating regulation that may pertain also to other Kv channels.

Discovery of novel HIV entry inhibitors for the CXCR4 receptor by prospective virtual screening

The process of HIV entry begins with the binding of the viral envelope glycoprotein gp120 to both the CD4 receptor and one of CXCR4 or CCR5 chemokine coreceptors. There is currently considerable interest in developing novel ligands which can attach to these coreceptors and hence block virus-cell fusion. This article compares the application of structure-based (docking) and ligand-based (QSAR analyses, pharmacophore modeling, and shape matching) virtual screening tools to find new potential HIV entry inhibitors for the CXCR4 receptor. The comparison is based on retrospective virtual screening of a library containing different known CXCR4 inhibitors from the literature, a smaller set of active CXCR4 inhibitors selected from a large combinatorial virtual library and synthesized by us, and some druglike presumed inactive molecules as the reference set. The enrichment factors and diversity of the retrieved molecular scaffolds in the virtual hit lists was determined. Once the different virtual screening approaches had been validated and the best parameters had been selected, prospective virtual screening of our virtual library was applied to identify new anti-HIV compounds using the same protocol as in the retrospective virtual screening analysis. The compounds selected using these computational tools were subsequently synthesized and assayed and showed activity values ranging from 4 to 0.022 microg/mL.

Potential for protein surface shape analysis using spherical harmonics and 3D Zernike descriptors

With structure databases expanding at a rapid rate, the task at hand is to provide reliable clues to their molecular function and to be able to do so on a large scale. This, however, requires suitable encodings of the molecular structure which are amenable to fast screening. To this end, moment-based representations provide a compact and nonredundant description of molecular shape and other associated properties. In this article, we present an overview of some commonly used representations with specific focus on two schemes namely spherical harmonics and their extension, the 3D Zernike descriptors. Key features and differences of the two are reviewed and selected applications are highlighted. We further discuss recent advances covering aspects of shape and property-based comparison at both global and local levels and demonstrate their applicability through some of our studies.

Fourier method for large scale surface modeling and registration

Spherical harmonic (SPHARM) description is a powerful Fourier shape modeling method for processing arbitrarily shaped but simply connected 3D objects. As a highly promising method, SPHARM has been widely used in several domains including medical imaging. However, its primary use has been focused on modeling small or moderately-sized surfaces that are relatively smooth, due to challenges related to its applicability, robustness and scalability. This paper presents an enhanced SPHARM framework that addresses these issues and show that the use of SPHARM can expand into broader areas. In particular, we present a simple and efficient Fourier expansion method on the sphere that enables large scale modeling, and propose a new SPHARM registration method that aims to preserve the important homological properties between 3D models. Although SPHARM is a global descriptor, our experimental results show that the proposed SPHARM framework can accurately describe complicated graphics models and highly convoluted 3D surfaces and the proposed registration method allows for effective alignment and registration of these 3D models for further processing or analysis. These methods greatly enable the potential of applying SPHARM to broader areas such as computer graphics, medical imaging, CAD/CAM, bioinformatics, and other related geometric modeling and processing fields.

Shape complementarity of protein-protein complexes at multiple resolutions

Biological complexes typically exhibit intermolecular interfaces of high shape complementarity. Many computational docking approaches use this surface complementarity as a guide in the search for predicting the structures of protein-protein complexes. Proteins often undergo conformational changes to create a highly complementary interface when associating. These conformational changes are a major cause of failure for automated docking procedures when predicting binding modes between proteins using their unbound conformations. Low resolution surfaces in which high frequency geometric details are omitted have been used to address this problem. These smoothed, or blurred, surfaces are expected to minimize the differences between free and bound structures, especially those that are due to side chain conformations or small backbone deviations. Despite the fact that this approach has been used in many docking protocols, there has yet to be a systematic study of the effects of such surface smoothing on the shape complementarity of the resulting interfaces. Here we investigate this question by computing shape complementarity of a set of 66 protein-protein complexes represented by multiresolution blurred surfaces. Complexed and unbound structures are available for these protein-protein complexes. They are a subset of complexes from a nonredundant docking benchmark selected for rigidity (i.e. the proteins undergo limited conformational changes between their bound and unbound states). In this work, we construct the surfaces by isocontouring a density map obtained by accumulating the densities of Gaussian functions placed at all atom centers of the molecule. The smoothness or resolution is specified by a Gaussian fall-off coefficient, termed "blobbyness." Shape complementarity is quantified using a histogram of the shortest distances between two proteins' surface mesh vertices for both the crystallographic complexes and the complexes built using the protein structures in their unbound conformation. The histograms calculated for the bound complex structures demonstrate that medium resolution smoothing (blobbyness = -0.9) can reproduce about 88% of the shape complementarity of atomic resolution surfaces. Complexes formed from the free component structures show a partial loss of shape complementarity (more overlaps and gaps) with the atomic resolution surfaces. For surfaces smoothed to low resolution (blobbyness = -0.3), we find more consistency of shape complementarity between the complexed and free cases. To further reduce bad contacts without significantly impacting the good contacts we introduce another blurred surface, in which the Gaussian densities of flexible atoms are reduced. From these results we discuss the use of shape complementarity in protein-protein docking.

Modeling three-dimensional morphological structures using spherical harmonics

Quantifying morphological shape is a fundamental issue in evolutionary biology. Recent technological advances (e.g., confocal microscopy, laser scanning, computer tomography) have made the capture of detailed three-dimensional (3D) morphological structure easy and cost-effective. In this article, we develop a 3D analytic framework (SPHARM-spherical harmonics) for modeling the shapes of complex morphological structures from continuous surface maps that can be produced by these technologies. Because the traditional SPHARM methodology has limitations in several of its processing steps, we present new algorithms for two SPHARM processing steps: spherical parameterization and SPHARM registration. These new algorithms allow for the numerical characterization of a much larger class of 3D models. We demonstrate the effectiveness of the method by applying it to modeling the cerci of Enallagma damselflies.

The structure of human extracellular copper-zinc superoxide dismutase at 1.7 A resolution: insights into heparin and collagen binding

Extracellular superoxide dismutase (SOD3) is a homotetrameric copper- and zinc-containing glycoprotein with affinity for heparin. The level of SOD3 is particularly high in blood vessel walls and in the lungs. The enzyme has multiple roles including protection of the lungs against hyperoxia and preservation of nitric oxide. The common mutation R213G, which reduces the heparin affinity of SOD3, is associated with increased risk of myocardial infarctions and stroke. We report the first crystal structure of human SOD3 at 1.7 A resolution. The overall subunit fold and the subunit-subunit interface of the SOD3 dimer are similar to the corresponding structures in Cu-Zn SOD (SOD1). The metal-binding sites are similar to those found in SOD1, but with Asn180 replacing Thr137 at the Cu-binding site and a much shorter loop at the zinc-binding site. The dimers form a functional homotetramer that is fashioned through contacts between two extended loops on each subunit. The N- and C-terminal end regions required for tetramerisation and heparin binding, respectively, are highly flexible. Two grooves fashioned by the tetramer interface are suggestive as the probable sites for heparin and collagen binding.

The application of 3D Zernike moments for the description of "model-free" molecular structure, functional motion, and structural reliability

Protein structures are not static entities consisting of equally well-determined atomic coordinates. Proteins undergo continuous motion, and as catalytic machines, these movements can be of high relevance for understanding function. In addition to this strong biological motivation for considering shape changes is the necessity to correctly capture different levels of detail and error in protein structures. Some parts of a structural model are often poorly defined, and the atomic displacement parameters provide an excellent means to characterize the confidence in an atom's spatial coordinates. A mathematical framework for studying these shape changes, and handling positional variance is therefore of high importance. We present an approach for capturing various protein structure properties in a concise mathematical framework that allows us to compare features in a highly efficient manner. We demonstrate how three-dimensional Zernike moments can be employed to describe functions, not only on the surface of a protein but throughout the entire molecule. A number of proof-of-principle examples are given which demonstrate how this approach may be used in practice for the representation of movement and uncertainty.