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Summary: We introduce WatFinder, a tool designed to identify and visualize protein-water interactions (water bridges, water-mediated associations, or water channels, fluxes, and clusters) relevant to protein stability, dynamics, and function. WatFinder is integrated into ProDy, a Python API broadly used for structure-based prediction of protein dynamics. WatFinder provides a suite of functions for generating raw data as well as outputs from statistical analyses. The ProDy framework facilitates comprehensive automation and efficient analysis of the ensembles of structures resolved for a given protein or the time-evolved conformations from simulations in explicit water, as illustrated in five case studies presented in the Supplementary Material., Availability and Implementation: ProDy is open-source and freely available under MIT License from https://github.com/ProDy/ProDy., (© The Author(s) 2024. Published by Oxford University Press.)

Summary: Efficient sampling of conformational space is essential for elucidating functional/allosteric mechanisms of proteins and generating ensembles of conformers for docking applications. However, unbiased sampling is still a challenge especially for highly flexible and/or large systems. To address this challenge, we describe a new implementation of our computationally efficient algorithm ClustENMD that is integrated with ProDy and OpenMM softwares. This hybrid method performs iterative cycles of conformer generation using elastic network model for deformations along global modes, followed by clustering and short molecular dynamics simulations. ProDy framework enables full automation and analysis of generated conformers and visualization of their distributions in the essential subspace., Availability and Implementation: ClustENMD is open-source and freely available under MIT License from https://github.com/prody/ProDy., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)

Summary: ProDy, an integrated application programming interface developed for modelling and analysing protein dynamics, has significantly evolved in recent years in response to the growing data and needs of the computational biology community. We present major developments that led to ProDy 2.0: (i) improved interfacing with databases and parsing new file formats, (ii) SignDy for signature dynamics of protein families, (iii) CryoDy for collective dynamics of supramolecular systems using cryo-EM density maps and (iv) essential site scanning analysis for identifying sites essential to modulating global dynamics., Availability and Implementation: ProDy is open-source and freely available under MIT License from https://github.com/prody/ProDy., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)

Summary: QuartataWeb is a user-friendly server developed for polypharmacological and chemogenomics analyses. Users can easily obtain information on experimentally verified (known) and computationally predicted (new) interactions between 5494 drugs and 2807 human proteins in DrugBank, and between 315 514 chemicals and 9457 human proteins in the STITCH database. In addition, QuartataWeb links targets to KEGG pathways and GO annotations, completing the bridge from drugs/chemicals to function via protein targets and cellular pathways. It allows users to query a series of chemicals, drug combinations or multiple targets, to enable multi-drug, multi-target, multi-pathway analyses, toward facilitating the design of polypharmacological treatments for complex diseases., Availability and Implementation: QuartataWeb is freely accessible at http://quartata.csb.pitt.edu., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

Motivation: The biological effects of human missense variants have been studied experimentally for decades but predicting their effects in clinical molecular diagnostics remains challenging. Available computational tools are usually based on the analysis of sequence conservation and structural properties of the mutant protein. We recently introduced a new machine learning method that demonstrated for the first time the significance of protein dynamics in determining the pathogenicity of missense variants., Results: Here, we present a new interface (Rhapsody) that enables fully automated assessment of pathogenicity, incorporating both sequence coevolution data and structure- and dynamics-based features. Benchmarked against a dataset of about 20 000 annotated variants, the methodology is shown to outperform well-established and/or advanced prediction tools. We illustrate the utility of Rhapsody by in silico saturation mutagenesis studies of human H-Ras, phosphatase and tensin homolog and thiopurine S-methyltransferase., Availability and Implementation: The new tool is available both as an online webserver at http://rhapsody.csb.pitt.edu and as an open-source Python package (GitHub repository: https://github.com/prody/rhapsody; PyPI package installation: pip install prody-rhapsody). Links to additional resources, tutorials and package documentation are provided in the 'Python package' section of the website., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

Motivation: With rapid accumulation of sequence data on several species, extracting rational and systematic information from multiple sequence alignments (MSAs) is becoming increasingly important. Currently, there is a plethora of computational methods for investigating coupled evolutionary changes in pairs of positions along the amino acid sequence, and making inferences on structure and function. Yet, the significance of coevolution signals remains to be established. Also, a large number of false positives (FPs) arise from insufficient MSA size, phylogenetic background and indirect couplings., Results: Here, a set of 16 pairs of non-interacting proteins is thoroughly examined to assess the effectiveness and limitations of different methods. The analysis shows that recent computationally expensive methods designed to remove biases from indirect couplings outperform others in detecting tertiary structural contacts as well as eliminating intermolecular FPs; whereas traditional methods such as mutual information benefit from refinements such as shuffling, while being highly efficient. Computations repeated with 2,330 pairs of protein families from the Negatome database corroborated these results. Finally, using a training dataset of 162 families of proteins, we propose a combined method that outperforms existing individual methods. Overall, the study provides simple guidelines towards the choice of suitable methods and strategies based on available MSA size and computing resources., Availability and Implementation: Software is freely available through the Evol component of ProDy API., (© The Author 2015. Published by Oxford University Press.)

Summary: The anisotropic network model (ANM) is one of the simplest yet powerful tools for exploring protein dynamics. Its main utility is to predict and visualize the collective motions of large complexes and assemblies near their equilibrium structures. The ANM server, introduced by us in 2006 helped making this tool more accessible to non-sophisticated users. We now provide a new version (ANM 2.0), which allows inclusion of nucleic acids and ligands in the network model and thus enables the investigation of the collective motions of protein-DNA/RNA and -ligand systems. The new version offers the flexibility of defining the system nodes and the interaction types and cutoffs. It also includes extensive improvements in hardware, software and graphical interfaces., Availability and Implementation: ANM 2.0 is available at http://anm.csb.pitt.edu, Contact: eran.eyal@sheba.health.gov.il, eyal.eran@gmail.com., (© The Author 2015. Published by Oxford University Press.)

Summary: BalestraWeb is an online server that allows users to instantly make predictions about the potential occurrence of interactions between any given drug-target pair, or predict the most likely interaction partners of any drug or target listed in the DrugBank. It also permits users to identify most similar drugs or most similar targets based on their interaction patterns. Outputs help to develop hypotheses about drug repurposing as well as potential side effects., Availability and Implementation: BalestraWeb is accessible at http://balestra.csb.pitt.edu/. The tool is built using a probabilistic matrix factorization method and DrugBank v3, and the latent variable models are trained using the GraphLab collaborative filtering toolkit. The server is implemented using Python, Flask, NumPy and SciPy., (© The Author 2014. Published by Oxford University Press.)

Unlabelled: Correlations between sequence evolution and structural dynamics are of utmost importance in understanding the molecular mechanisms of function and their evolution. We have integrated Evol, a new package for fast and efficient comparative analysis of evolutionary patterns and conformational dynamics, into ProDy, a computational toolbox designed for inferring protein dynamics from experimental and theoretical data. Using information-theoretic approaches, Evol coanalyzes conservation and coevolution profiles extracted from multiple sequence alignments of protein families with their inferred dynamics., Availability and Implementation: ProDy and Evol are open-source and freely available under MIT License from http://prody.csb.pitt.edu/., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Summary: We developed a Python package, ProDy, for structure-based analysis of protein dynamics. ProDy allows for quantitative characterization of structural variations in heterogeneous datasets of structures experimentally resolved for a given biomolecular system, and for comparison of these variations with the theoretically predicted equilibrium dynamics. Datasets include structural ensembles for a given family or subfamily of proteins, their mutants and sequence homologues, in the presence/absence of their substrates, ligands or inhibitors. Numerous helper functions enable comparative analysis of experimental and theoretical data, and visualization of the principal changes in conformations that are accessible in different functional states. ProDy application programming interface (API) has been designed so that users can easily extend the software and implement new methods., Availability: ProDy is open source and freely available under GNU General Public License from http://www.csb.pitt.edu/ProDy/.

Motivation: To efficiently analyze the 'native ensemble of conformations' accessible to proteins near their folded state and to extract essential information from observed distributions of conformations, reliable mathematical methods and computational tools are needed., Result: Examination of 24 pairs of structures determined by both NMR and X-ray reveals that the differences in the dynamics of the same protein resolved by the two techniques can be tracked to the most robust low frequency modes elucidated by principal component analysis (PCA) of NMR models. The active sites of enzymes are found to be highly constrained in these PCA modes. Furthermore, the residues predicted to be highly immobile are shown to be evolutionarily conserved, lending support to a PCA-based identification of potential functional sites. An online tool, PCA_NEST, is designed to derive the principal modes of conformational changes from structural ensembles resolved by experiments or generated by computations., Availability: http://ignm.ccbb.pitt.edu/oPCA_Online.htm

Motivation: The ability of human immunodeficiency virus-1 (HIV-1) protease to develop mutations that confer multi-drug resistance (MDR) has been a major obstacle in designing rational therapies against HIV. Resistance is usually imparted by a cooperative mechanism that can be elucidated by a covariance analysis of sequence data. Identification of such correlated substitutions of amino acids may be obscured by evolutionary noise., Results: HIV-1 protease sequences from patients subjected to different specific treatments (set 1), and from untreated patients (set 2) were subjected to sequence covariance analysis by evaluating the mutual information (MI) between all residue pairs. Spectral clustering of the resulting covariance matrices disclosed two distinctive clusters of correlated residues: the first, observed in set 1 but absent in set 2, contained residues involved in MDR acquisition; and the second, included those residues differentiated in the various HIV-1 protease subtypes, shortly referred to as the phylogenetic cluster. The MDR cluster occupies sites close to the central symmetry axis of the enzyme, which overlap with the global hinge region identified from coarse-grained normal-mode analysis of the enzyme structure. The phylogenetic cluster, on the other hand, occupies solvent-exposed and highly mobile regions. This study demonstrates (i) the possibility of distinguishing between the correlated substitutions resulting from neutral mutations and those induced by MDR upon appropriate clustering analysis of sequence covariance data and (ii) a connection between global dynamics and functional substitution of amino acids.

Unlabelled: Identification of correlated amino acids in proteins has been a topic of broad interest in view of its functional implications and importance in protein design. A new set of pair-to-pair (P2P) substitution matrices for amino acids was recently introduced as a useful tool for inferring information on such correlated sites. We present a website developed for automated application of these matrices for analysis of query sequences. The site offers options for graphical analysis of correlations, as well as visualization of correlated amino acids on representative, structurally characterized, members of the examined family of sequences., Availability: http://www.ccbb.pitt.edu/p2p.

Motivation: A common practice in X-ray crystallographic structure refinement has been to model atomic displacements or thermal fluctuations as isotropic motions. Recent high-resolution data reveal, however, significant departures from isotropy, described by anisotropic displacement parameters (ADPs) modeled for individual atoms. Yet, ADPs are currently reported for a limited set of structures, only., Results: We present a comparative analysis of the experimentally reported ADPs and those theoretically predicted by the anisotropic network model (ANM) for a representative set of structures. The relative sizes of fluctuations along different directions are shown to agree well between experiments and theory, while the cross-correlations between the (x-, y- and z-) components of the fluctuations show considerable deviations. Secondary structure elements and protein cores exhibit more robust anisotropic characteristics compared to disordered or flexible regions. The deviations between experimental and theoretical data are comparable to those between sets of experimental ADPs reported for the same protein in different crystal forms. These results draw attention to the effects of crystal form and refinement procedure on experimental ADPs and highlight the potential utility of ANM calculations for consolidating experimental data or assessing ADPs in the absence of experimental data., Availability: The ANM server at http://www.ccbb.pitt.edu/anm is upgraded to permit users to compute and visualize the theoretical ADPs for any PDB structure, thus providing insights into the anisotropic motions intrinsically preferred by equilibrium structures., Supplementary Information: Two Supplementary Material files can be accessed at the journal website. The first presents the tabulated results from computations (Pearson correlations and KL distances with respect to experimental ADPs) reported for each of the 93 proteins in Set I (the averages over all proteins are presented above in Table 3). The second file consists of three sections: (A) detailed derivation of Equation (7), (B) analysis of the effect of ANM parameters on computed ADPs and identification of parameters that achieve optimal correlation with experiments and (C) description of the method for computing the tangential and radial components of equilibrium fluctuations.

Motivation: The Anisotropic Network Model (ANM) is a simple yet powerful model for normal mode analysis of proteins. Despite its broad use for exploring biomolecular collective motions, ANM has not been systematically evaluated to date. A lack of a convenient interface has been an additional obstacle for easy usage., Results: ANM has been evaluated on a large set of proteins to establish the optimal model parameters that achieve the highest correlation with experimental data and its limits of accuracy and applicability. Residue fluctuations in globular proteins are shown to be more accurately predicted than those in nonglobular proteins, and core residues are more accurately described than solvent-exposed ones. Significant improvement in agreement with experiments is observed with increase in the resolution of the examined structure. A new server for ANM calculations is presented, which offers flexible options for controlling model parameters and output formats, interactive animation of collective modes and advanced graphical features., Availability: ANM server (http://www.ccbb.pitt.edu/anm)

Motivation: The knowledge of protein structure is not sufficient for understanding and controlling its function. Function is a dynamic property. Although protein structural information has been rapidly accumulating in databases, little effort has been invested to date toward systematically characterizing protein dynamics. The recent success of analytical methods based on elastic network models, and in particular the Gaussian Network Model (GNM), permits us to perform a high-throughput analysis of the collective dynamics of proteins., Results: We computed the GNM dynamics for 20 058 structures from the Protein Data Bank, and generated information on the equilibrium dynamics at the level of individual residues. The results are stored on a web-based system called iGNM and configured so as to permit the users to visualize or download the results through a standard web browser using a simple search engine. Static and animated images for describing the conformational mobility of proteins over a broad range of normal modes are accessible, along with an online calculation engine available for newly deposited structures. A case study of the dynamics of 20 non-homologous hydrolases is presented to illustrate the utility of the iGNM database for identifying key residues that control the cooperative motions and revealing the connection between collective dynamics and catalytic activity.

Motivation: Analysis of protein sequence and structure databases usually reveal frequent patterns (FP) associated with biological function. Data mining techniques generally consider the physicochemical and structural properties of amino acids and their microenvironment in the folded structures. Dynamics is not usually considered, although proteins are not static, and their function relates to conformational mobility in many cases., Results: This work describes a novel unsupervised learning approach to discover FPs in the protein families, based on biochemical, geometric and dynamic features. Without any prior knowledge of functional motifs, the method discovers the FPs for each type of amino acid and identifies the conserved residues in three protease subfamilies; chymotrypsin and subtilisin subfamilies of serine proteases and papain subfamily of cysteine proteases. The catalytic triad residues are distinguished by their strong spatial coupling (high interconnectivity) to other conserved residues. Although the spatial arrangements of the catalytic residues in the two subfamilies of serine proteases are similar, their FPs are found to be quite different. The present approach appears to be a promising tool for detecting functional patterns in rapidly growing structure databases and providing insights in to the relationship among protein structure, dynamics and function., Availability: Available upon request from the authors.