Domain-based small molecule binding site annotation

Background  

Accurate small molecule binding site information for a protein can facilitate studies in drug docking, drug discovery and function prediction, but small molecule binding site protein sequence annotation is sparse. The Small Molecule Interaction Database (SMID), a database of protein domain-small molecule interactions, was created using structural data from the Protein Data Bank (PDB). More importantly it provides a means to predict small molecule binding sites on proteins with a known or unknown structure and unlike prior approaches, removes large numbers of false positive hits arising from transitive alignment errors, non-biologically significant small molecules and crystallographic conditions that overpredict ion binding sites.     

PSSM-based prediction of DNA binding sites in proteins

Background  

Detection of DNA-binding sites in proteins is of enormous interest for technologies targeting gene regulation and manipulation. We have previously shown that a residue and its sequence neighbor information can be used to predict DNA-binding candidates in a protein sequence. This sequence-based prediction method is applicable even if no sequence homology with a previously known DNA-binding protein is observed. Here we implement a neural network based algorithm to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.     

Identification and utilization of arbitrary correlations in models of recombination signal sequences

Background  

A significant challenge in bioinformatics is to develop methods for detecting and modeling patterns in variable DNA sequence sites, such as protein-binding sites in regulatory DNA. Current approaches sometimes perform poorly when positions in the site do not independently affect protein binding. We developed a statistical technique for modeling the correlation structure in variable DNA sequence sites. The method places no restrictions on the number of correlated positions or on their spatial relationship within the site. No prior empirical evidence for the correlation structure is necessary.     

Automatic generation of 3D motifs for classification of protein binding sites

Background  

Since many of the new protein structures delivered by high-throughput processes do not have any known function, there is a need for structure-based prediction of protein function. Protein 3D structures can be clustered according to their fold or secondary structures to produce classes of some functional significance. A recent alternative has been to detect specific 3D motifs which are often associated to active sites. Unfortunately, there are very few known 3D motifs, which are usually the result of a manual process, compared to the number of sequential motifs already known. In this paper, we report a method to automatically generate 3D motifs of protein structure binding sites based on consensus atom positions and evaluate it on a set of adenine based ligands.     

A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction

Background  

Predicting which molecules can bind to a given binding site of a protein with known 3D structure is important to decipher the protein function, and useful in drug design. A classical assumption in structural biology is that proteins with similar 3D structures have related molecular functions, and therefore may bind similar ligands. However, proteins that do not display any overall sequence or structure similarity may also bind similar ligands if they contain similar binding sites. Quantitatively assessing the similarity between binding sites may therefore be useful to propose new ligands for a given pocket, based on those known for similar pockets.     

Prediction of protein binding sites in protein structures using hidden Markov support vector machine

Background  

Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance.     

A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction

Background  

Current scoring functions are not very successful in protein-ligand binding affinity prediction albeit their popularity in structure-based drug designs. Here, we propose a general knowledge-guided scoring (KGS) strategy to tackle this problem. Our KGS strategy computes the binding constant of a given protein-ligand complex based on the known binding constant of an appropriate reference complex. A good training set that includes a sufficient number of protein-ligand complexes with known binding data needs to be supplied for finding the reference complex. The reference complex is required to share a similar pattern of key protein-ligand interactions to that of the complex of interest. Thus, some uncertain factors in protein-ligand binding may cancel out, resulting in a more accurate prediction of absolute binding constants.     

Prediction of splice sites with dependency graphs and their expanded bayesian networks

MOTIVATION: Owing to the complete sequencing of human and many other genomes, huge amounts of DNA sequence data have been accumulated. In bioinformatics, an important issue is how to predict the complete structure of genes from the genomic DNA sequence, especially the human genome. A crucial part in the gene structure prediction is to determine the precise exon-intron boundaries, i.e. the splice sites, in the coding region. RESULTS: We have developed a dependency graph model to fully capture the intrinsic interdependency between base positions in a splice site. The establishment of dependency between two position is based on a chi2-test from known sample data. To facilitate statistical inference, we have expanded the dependency graph (which is usually a graph with cycles that make probabilistic reasoning very difficult, if not impossible) into a Bayesian network (which is a directed acyclic graph that facilitates statistical reasoning).When compared with the existing models such as weight matrix model, weight array model, maximal dependence decomposition, Cai et al.'s tree model as well as the less-studied second-order and third-order Markov chain models, the expanded Bayesian networks from our dependency graph models perform the best in nearly all the cases studied. AVAILABILITY: Software (a program called DGSplicer) and datasets used are available at http://csrl.ee.nthu.edu.tw/bioinf/ CONTACT: cclu@ee.nthu.edu.tw.     

DNA deformability changes of single base pair mutants within CDE binding sites in S. Cerevisiae centromere DNA correlate with measured chromosomal loss rates and CDE binding site symmetries

Background  

The centromeres in yeast (S. cerevisiae) are organized by short DNA sequences (125 bp) on each chromosome consisting of 2 conserved elements: CDEI and CDEIII spaced by a CDEII region. CDEI and CDEIII are critical sequence specific protein binding sites necessary for correct centromere formation and following assembly with proteins, are positioned near each other on a specialized nucleosome. Hegemann et al. BioEssays 1993, 15: 451–460 reported single base DNA mutants within the critical CDEI and CDEIII binding sites on the centromere of chromosome 6 and quantitated centromere loss of function, which they measured as loss rates for the different chromosome 6 mutants during cell division. Olson et al. Proc Natl Acad Sci USA 1998, 95: 11163–11168 reported the use of protein-DNA crystallography data to produce a DNA dinucleotide protein deformability energetic scale (PD-scale) that describes local DNA deformability by sequence specific binding proteins. We have used the PD-scale to investigate the DNA sequence dependence of the yeast chromosome 6 mutants' loss rate data. Each single base mutant changes 2 PD-scale values at that changed base position relative to the wild type. In this study, we have utilized these mutants to demonstrate a correlation between the change in DNA deformability of the CDEI and CDEIII core sites and the overall experimentally measured chromosome loss rates of the chromosome 6 mutants.     

Oxidation by DNA charge transport damages conserved sequence block II, a regulatory element in mitochondrial DNA

Sites of oxidative damage in mitochondrial DNA have been identified on the basis of DNA-mediated charge transport. Our goal is to understand which sites in mitochondrial DNA are prone to oxidation at long range and whether such oxidative damage correlates with cancerous transformation. Here we show that a primer extension reaction can be used to monitor directly oxidative damage to authentic mitochondrial DNA through photoreactions with a rhodium intercalator. The complex [Rh(phi)2bpy]Cl3 (phi = 9,10-phenanthrenequinone diimine) binds to DNA without sequence specificity and, upon photoactivation, either promotes strand breaks directly at the binding site or promotes one-electron oxidative damage; comparing the sites of base oxidation to direct strand breaks reveals the oxidative damage that arises from a distance through DNA-mediated charge transport. Significantly, base oxidation by charge transport overlaps with known mutational hot spots associated with cancers at nucleotides surrounding positions 263 and 303; the latter is known as conserved sequence block II and is vital to DNA replication. Since DNA base oxidation at conserved sequence block II should weaken the ability of damaged mitochondrial genomes to be replicated, DNA-mediated charge transport may provide a protection mechanism for excluding damaged DNA.     

Fragment-based identification of determinants of conformational and spectroscopic change at the ricin active site

Background  

Ricin is a potent toxin and known bioterrorism threat with no available antidote. The ricin A-chain (RTA) acts enzymatically to cleave a specific adenine base from ribosomal RNA, thereby blocking translation. To understand better the relationship between ligand binding and RTA active site conformational change, we used a fragment-based approach to find a minimal set of bonding interactions able to induce rearrangements in critical side-chain positions.