A novel algorithm for non-bonded-list updating in molecular simulations.

Simulations of molecular systems typically handle interactions within non-bonded pairs. Generating and updating a list of these pairs can be the most time-consuming part of energy calculations for large systems. Thus, efficient non-bonded list processing can speed up the energy calculations significantly. While the asymptotic complexity of current algorithms (namely O(N), where N is the number of particles) is probably the lowest possible, a wide space for optimization is still left. This article offers a heuristic extension to the previously suggested grid based algorithms. We show that, when the average particle movements are slow, simulation time can be reduced considerably. The proposed algorithm has been implemented in the DistanceMatrix class of the molecular modeling package MESHI. MESHI is freely available at .     

RNAthermsw: Direct Temperature Simulations for Predicting the Location of RNA Thermometers

The mechanism of RNA thermometers is a subject of growing interest. Also known as RNA thermosensors, these temperature-sensitive segments of the mRNA regulate gene expression by changing their secondary structure in response to temperature fluctuations. The detection of RNA thermometers in various genes of interest is valuable as it could lead to the discovery of new thermometers participating in fundamental processes such as preferential translation during heat-shock. RNAthermsw is a user-friendly webserver for predicting the location of RNA thermometers using direct temperature simulations. It operates by analyzing dotted figures generated as a result of a moving window that performs successive energy minimization folding predictions. Inputs include the RNA sequence, window size, and desired temperature change. RNAthermsw can be freely accessed at http://www.cs.bgu.ac.il/~rnathemsw/RNAthemsw/ (with the slash sign at the end). The website contains a help page with explanations regarding the exact usage.     

Hierarchical tree snipping: clustering guided by prior knowledge

MOTIVATION: Hierarchical clustering is widely used to cluster genes into groups based on their expression similarity. This method first constructs a tree. Next this tree is partitioned into subtrees by cutting all edges at some level, thereby inducing a clustering. Unfortunately, the resulting clusters often do not exhibit significant functional coherence. RESULTS: To improve the biological significance of the clustering, we develop a new framework of partitioning by snipping--cutting selected edges at variable levels. The snipped edges are selected to induce clusters that are maximally consistent with partially available background knowledge such as functional classifications. Algorithms for two key applications are presented: functional prediction of genes, and discovery of functionally enriched clusters of co-expressed genes. Simulation results and cross-validation tests indicate that the algorithms perform well even when the actual number of clusters differs considerably from the requested number. Performance is improved compared with a previously proposed algorithm. AVAILABILITY: A java package is available at http://www.cs.bgu.ac.il/~dotna/ TreeSnipping     

Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data

We present and review coupled two-way clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis. AVAILABILITY: Free, at http://ctwc.weizmann.ac.il.. SUPPLEMENTARY INFORMATION: http://www.weizmann.ac.il/physics/complex/compphys/bioinfo2/     

F2CS: FSSP to CATH and SCOP prediction server

The F2CS server provides access to the software, F2CS2.00, which implements an automated prediction method of SCOP and CATH classifications of proteins, based on their FSSP Z-scores. AVAILABILITY: Free at http://www.weizmann.ac.il/physics/complex/compphys/f2cs/ SUPPLEMENTARY INFORMATION: The site contains links to additional figures and tables.     

Selecton: a server for detecting evolutionary forces at a single amino-acid site

SUMMARY: We present an algorithmic tool for the identification of biologically significant amino acids in proteins of known three dimensional structure. We estimate the degree of purifying selection and positive Darwinian selection at each site and project these estimates onto the molecular surface of the protein. Thus, patches of functional residues (undergoing either positive or purifying selection), which may be discontinuous in the linear sequence, are revealed. We test for the statistical significance of the site-specific scores in order to obtain reliable and valid estimates. AVAILABILITY: The Selecton web server is available at: http://selecton.bioinfo.tau.ac.il SUPPLEMENTARY INFORMATION: More information is available at http://selecton.bioinfo.tau.ac.il/overview.html. A set of examples is available at http://selecton.bioinfo.tau.ac.il/gallery.html.     

TAMBIS: transparent access to multiple bioinformatics information sources

SUMMARY: TAMBIS (Transparent Access to Multiple Bioinformatics Information Sources) is an application that allows biologists to ask rich and complex questions over a range of bioinformatics resources. It is based on a model of the knowledge of the concepts and their relationships in molecular biology and bioinformatics. AVAILABILITY: TAMBIS is available as an applet from http://img.cs.man.ac.uk/tambis SUPPLEMENTARY: A full manual, tutorial and videos can be found at http://img.cs.man.ac.uk/tambis. CONTACT: tambis@cs.man.ac.uk     

WebPropagate: A Web Server for Network Propagation

Network propagation is a powerful tool for genetic analysis which is widely used to identify genes and genetic modules that underlie a process of interest. Here we provide a graphical, web-based platform (http://anat.cs.tau.ac.il/WebPropagate/) in which researchers can easily apply variants of this method to data sets of interest using up-to-date networks of protein–protein interactions in several organisms.     

HingeProt: automated prediction of hinges in protein structures

Proteins are highly flexible molecules. Prediction of molecular flexibility aids in the comprehension and prediction of protein function and in providing details of functional mechanisms. The ability to predict the locations, directions, and extent of molecular movements can assist in fitting atomic resolution structures to low-resolution EM density maps and in predicting the complex structures of interacting molecules (docking). There are several types of molecular movements. In this work, we focus on the prediction of hinge movements. Given a single protein structure, the method automatically divides it into the rigid parts and the hinge regions connecting them. The method employs the Elastic Network Model, which is very efficient and was validated against a large data set of proteins. The output can be used in applications such as flexible protein-protein and protein-ligand docking, flexible docking of protein structures into cryo-EM maps, and refinement of low-resolution EM structures. The web server of HingeProt provides convenient visualization of the results and is available with two mirror sites at http://www.prc.boun.edu.tr/appserv/prc/HingeProt3 and http://bioinfo3d.cs.tau.ac.il/HingeProt/.     

Structure-based identification of catalytic residues

The identification of catalytic residues is an essential step in functional characterization of enzymes. We present a purely structural approach to this problem, which is motivated by the difficulty of evolution-based methods to annotate structural genomics targets that have few or no homologs in the databases. Our approach combines a state-of-the-art support vector machine (SVM) classifier with novel structural features that augment structural clues by spatial averaging and Z scoring. Special attention is paid to the class imbalance problem that stems from the overwhelming number of non-catalytic residues in enzymes compared to catalytic residues. This problem is tackled by: (1) optimizing the classifier to maximize a performance criterion that considers both Type I and Type II errors in the classification of catalytic and non-catalytic residues; (2) under-sampling non-catalytic residues before SVM training; and (3) during SVM training, penalizing errors in learning catalytic residues more than errors in learning non-catalytic residues. Tested on four enzyme datasets, one specifically designed by us to mimic the structural genomics scenario and three previously evaluated datasets, our structure-based classifier is never inferior to similar structure-based classifiers and comparable to classifiers that use both structural and evolutionary features. In addition to the evaluation of the performance of catalytic residue identification, we also present detailed case studies on three proteins. This analysis suggests that many false positive predictions may correspond to binding sites and other functional residues. A web server that implements the method, our own-designed database, and the source code of the programs are publicly available at http://www.cs.bgu.ac.il/～meshi/functionPrediction.     

Automated analysis of interatomic contacts in proteins.

MOTIVATION: New software has been designed to assist the molecular biologist in understanding the structural consequences of modifying a ligand and/or protein. RESULTS: Tools are described for the analysis of ligand-protein contacts (LPC software) and contacts of structural units (CSU software) such as helices, sheets, strands and residues. Our approach is based on a detailed analysis of interatomic contacts and interface complementarity. For any ligand or structural unit, these software automatically: (i) calculate the solvent-accessible surface of every atom; (ii) determine the contacting residues and type of interaction they undergo (hydrophobic-hydrophobic, aromatic-aromatic, etc.); (iii) indicate all putative hydrogen bonds. LPC software further predicts changes in binding strength following chemical modification of the ligand. AVAILABILITY: Both LPC and CSU can be accessed through the PDB and are integrated in the 3DB Atlas page of all PDB files. For any given file, the tools can also be accessed at http://www.pdb.bnl. gov/pdb-bin/lpc?PDB_ID= and http://www.pdb.bnl. gov/pdb-bin/csu?PDB_ID= with the four-letter PDB code added at the end in each case. Finally, LPC and CSU can be accessed at: http://sgedg.weizmann.ac.il/lpc and http://sgedg.weizmann.ac.il/csu.     

Machaon CVE: cluster validation for gene expression data

SUMMARY: This paper presents a cluster validation tool for gene expression data. Machaon CVE (Clustering and Validation Environment) system aims to partition samples or genes into groups characterized by similar expression patterns, and to evaluate the quality of the clusters obtained. AVAILABILITY: The program is freely available for non-profit use on request at http://www.cs.tcd.ie/Nadia.Bolshakova/Machaon.html SUPPLEMENTARY INFORMATION: http://www.cs.tcd.ie/Nadia.Bolshakova/Machaon.html     

ScanNet: A Web Server for Structure-based Prediction of Protein Binding Sites with Geometric Deep Learning

Predicting the various binding sites of a protein from its structure sheds light on its function and paves the way towards design of interaction inhibitors. Here, we report ScanNet, a freely available web server for prediction of protein–protein, protein - disordered protein and protein - antibody binding sites from structure. ScanNet (Spatio-Chemical Arrangement of Neighbors Network) is an end-to-end, interpretable geometric deep learning model that learns spatio-chemical patterns directly from 3D structures. ScanNet consistently outperforms Machine Learning models based on handcrafted features and comparative modeling approaches. The web server is linked to both the PDB and AlphaFoldDB, and supports user-provided structure files. Predictions can be readily visualized on the website via the Molstar web app and locally via ChimeraX. ScanNet is available at http://bioinfo3d.cs.tau.ac.il/ScanNet/.     

ConSurf: identification of functional regions in proteins by surface-mapping of phylogenetic information

SUMMARY: We recently developed algorithmic tools for the identification of functionally important regions in proteins of known three dimensional structure by estimating the degree of conservation of the amino-acid sites among their close sequence homologues. Projecting the conservation grades onto the molecular surface of these proteins reveals patches of highly conserved (or occasionally highly variable) residues that are often of important biological function. We present a new web server, ConSurf, which automates these algorithmic tools. ConSurf may be used for high-throughput characterization of functional regions in proteins. AVAILABILITY: The ConSurf web server is available at:http://consurf.tau.ac.il. SUPPLEMENTARY INFORMATION: A set of examples is available at http://consurf.tau.ac.il under 'GALLERY'.     

PartiGene--constructing partial genomes

Expressed sequence tags (ESTs) offer a low-cost approach to gene discovery and are being used by an increasing number of laboratories to obtain sequence information for a wide variety of organisms. The challenge lies in processing and organizing this data within a genomic context to facilitate large scale analyses. Here we present PartiGene, an integrated sequence analysis suite that uses freely available public domain software to (1) process raw trace chromatograms into sequence objects suitable for submission to dbEST; (2) place these sequences within a genomic context; (3) perform customizable first-pass annotation of the data; and (4) present the data as HTML tables and an SQL database resource. PartiGene has been used to create a number of non-model organism database resources including NEMBASE (http://www.nematodes.org) and LumbriBase (http://www.earthworms.org/). The packages are readily portable, freely available and can be run on simple Linux-based workstations. AVAILABILITY: PartiGene is available from http://www.nematodes.org/PartiGene and also forms part of the EST analysis software, associated with the Natural Environmental Research Council (UK) Bio-Linux project (http://envgen.nox.ac.uk/biolinux.html).     

Structural systems identification of genetic regulatory networks

MOTIVATION: Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit. RESULTS: In this report, we extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles. We applied our method to synthetic data and to SOS data and showed that our method significantly outperforms the regular EM without structural constraints. AVAILABILITY: The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his website, http://www.che.udel.edu/systems/people/zak.     

SARGE: a tool for creation of putative genetic networks

SUMMARY: SARGE is a tool for creating, visualizing and manipulating a putative genetic network from time series microarray data. The tool assigns potential edges through time-lagged correlation, incorporates a clustering mechanism, an interactive visual graph representation and employs simulated annealing for network optimization. AVAILABILITY: The application is available as a .jar file from http://www.bioinformatics.cs.ncl.ac.uk/sarge/index.html.     

The Bioinformatics Template Library--generic components for biocomputing

MOTIVATION: The efficiency of bioinformatics programmers can be greatly increased through the provision of ready-made software components that can be rapidly combined, with additional bespoke components where necessary, to create finished programs. The new standard for C++ includes an efficient and easy to use library of generic algorithms and data-structures, designed to facilitate low-level component programming. The extension of this library to include functionality that is specifically useful in compute-intensive tasks in bioinformatics and molecular modelling could provide an effective standard for the design of reusable software components within the biocomputing community. RESULTS: A novel application of generic programming techniques in the form of a library of C++ components called the Bioinformatics Template Library (BTL) is presented. This library will facilitate the rapid development of efficient programs by providing efficient code for many algorithms and data-structures that are commonly used in biocomputing, in a generic form that allows them to be flexibly combined with application specific object-oriented class libraries. AVAILABILITY: The BTL is available free of charge from our web site http://www.cryst.bbk.ac.uk/~classlib/ and the EMBL file server http://www.embl-ebi.ac.uk/FTP/index.html