MOTIVATION: The need for new drugs and new targets is particularly compelling in an era that is witnessing an alarming increase of drug resistance in human pathogens. The identification of new targets of known drugs is a promising approach, which has proven successful in several cases. Here, we describe a database that includes information on 5153 putative drug-target pairs for 150 human pathogens derived from available drug-target crystallographic complexes. AVAILABILITY AND IMPLEMENTATION: The TiPs database is freely available at http://biocomputing.it/tips. CONTACT: firstname.lastname@example.org or email@example.com.
MOTIVATION: The mutation of amino acids often impacts protein function and structure. Mutations without negative effect sustain evolutionary pressure. We study a particular aspect of structural robustness with respect to mutations: regular protein secondary structure and natively unstructured (intrinsically disordered) regions. Is the formation of regular secondary structure an intrinsic feature of amino acid sequences, or is it a feature that is lost upon mutation and is maintained by evolution against the odds? Similarly, is disorder an intrinsic sequence feature or is it difficult to maintain? To tackle these questions, we in silico mutated native protein sequences into random sequence-like ensembles and monitored the change in predicted secondary structure and disorder. RESULTS: We established that by our coarse-grained measures for change, predictions and observations were similar, suggesting that our results were not biased by prediction mistakes. Changes in secondary structure and disorder predictions were linearly proportional to the change in sequence. Surprisingly, neither the content nor the length distribution for the predicted secondary structure changed substantially. Regions with long disorder behaved differently in that significantly fewer such regions were predicted after a few mutation steps. Our findings suggest that the formation of regular secondary structure is an intrinsic feature of random amino acid sequences, while the formation of long-disordered regions is not an intrinsic feature of proteins with disordered regions. Put differently, helices and strands appear to be maintained easily by evolution, whereas maintaining disordered regions appears difficult. Neutral mutations with respect to disorder are therefore very unlikely.
MOTIVATION: Antibodies or immunoglobulins are proteins of paramount importance in the immune system. They are extremely relevant as diagnostic, biotechnological and therapeutic tools. Their modular structure makes it easy to re-engineer them for specific purposes. Short of undergoing a trial and error process, these experiments, as well as others, need to rely on an understanding of the specific determinants of the antibody binding mode. RESULTS: In this article, we present a method to identify, on the basis of the antibody sequence alone, which residues of an antibody directly interact with its cognate antigen. The method, based on the random forest automatic learning techniques, reaches a recall and specificity as high as 80% and is implemented as a free and easy-to-use server, named prediction of Antibody Contacts. We believe that it can be of great help in re-design experiments as well as a guide for molecular docking experiments. The results that we obtained also allowed us to dissect which features of the antibody sequence contribute most to the involvement of specific residues in binding to the antigen. AVAILABILITY: http://www.biocomputing.it/proABC. CONTACT: firstname.lastname@example.org or email@example.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: Antibodies are rapidly becoming essential tools in the clinical practice, given their ability to recognize their cognate antigens with high specificity and affinity, and a high yield at reasonable costs in model animals. Unfortunately, when administered to human patients, xenogeneic antibodies can elicit unwanted and dangerous immunogenic responses. Antibody humanization methods are designed to produce molecules with a better safety profile still maintaining their ability to bind the antigen. This can be accomplished by grafting the non-human regions determining the antigen specificity into a suitable human template. Unfortunately, this procedure may results in a partial or complete loss of affinity of the grafted molecule that can be restored by back-mutating some of the residues of human origin to the corresponding murine ones. This trial-and-error procedure is hard and involves expensive and time-consuming experiments. Here we present tools for antibody humanization (Tabhu) a web server for antibody humanization. Tabhu includes tools for human template selection, grafting, back-mutation evaluation, antibody modelling and structural analysis, helping the user in all the critical steps of the humanization experiment protocol. AVAILABILITY: http://www.biocomputing.it/tabhu CONTACT: firstname.lastname@example.org, email@example.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Le Pera, Loredana; Marcatili, Paolo; Tramontano, Anna(Bioinformatics, Oxford University Press (OUP), 2010-10-12)[Article]
MOTIVATION: Several international collaborations and local projects are producing extensive catalogues of genomic variations that are supplementing existing collections such as the OMIM catalogue. The flood of this type of data will keep increasing and, especially, it will be relevant to a wider user base, including not only molecular biologists, geneticists and bioinformaticians, but also clinical researchers. Mapping the observed variations, sometimes only described at the amino acid level, on a genome, identifying whether they affect a gene and-if so-whether they also affect different isoforms of the same gene, is a time consuming and often frustrating task. RESULTS: The PICMI server is an easy to use tool for quickly mapping one or more amino acid or nucleotide variations on a genome and its products, including alternatively spliced isoforms. AVAILABILITY: The server is available at www.biocomputing.it/picmi.
Le Pera, Loredana; Mazzapioda, Mariagiovanna; Tramontano, Anna(Bioinformatics, Oxford University Press (OUP), 2015-01-22)[Article]
Protein-coding genes with multiple alternative polyadenylation sites can generate mRNA 3'UTR sequences of different lengths, thereby causing the loss or gain of regulatory elements, which can affect stability, localization and translation efficiency. 3USS is a web-server developed with the aim of giving experimentalists the possibility to automatically identify alternative 3 ': UTRs (shorter or longer with respect to a reference transcriptome), an option that is not available in standard RNA-seq data analysis procedures. The tool reports as putative novel the 3 ': UTRs not annotated in available databases. Furthermore, if data from two related samples are uploaded, common and specific alternative 3 ': UTRs are identified and reported by the server.3USS is freely available at http://www.biocomputing.it/3uss_server.
Molecular dynamics (MD) simulation is a well-established method for studying protein motion at the atomic scale. However, it is computationally intensive and generates massive amounts of data. One way of addressing the dual challenges of computation efficiency and data analysis is to construct simplified models of long-timescale protein motion from MD simulation data. In this direction, we propose to use Markov models with hidden states, in which the Markovian states represent potentially overlapping probabilistic distributions over protein conformations. We also propose a principled criterion for evaluating the quality of a model by its ability to predict long-timescale protein motions. Our method was tested on 2D synthetic energy landscapes and two extensively studied peptides, alanine dipeptide and the villin headpiece subdomain (HP-35 NleNle). One interesting finding is that although a widely accepted model of alanine dipeptide contains six states, a simpler model with only three states is equally good for predicting long-timescale motions. We also used the constructed Markov models to estimate important kinetic and dynamic quantities for protein folding, in particular, mean first-passage time. The results are consistent with available experimental measurements.
Messih, Mario Abdel; Lepore, Rosalba; Marcatili, Paolo; Tramontano, Anna(Bioinformatics, Oxford University Press (OUP), 2014-06-13)[Article]
MOTIVATION: Antibodies are able to recognize a wide range of antigens through their complementary determining regions formed by six hypervariable loops. Predicting the 3D structure of these loops is essential for the analysis and reengineering of novel antibodies with enhanced affinity and specificity. The canonical structure model allows high accuracy prediction for five of the loops. The third loop of the heavy chain, H3, is the hardest to predict because of its diversity in structure, length and sequence composition. RESULTS: We describe a method, based on the Random Forest automatic learning technique, to select structural templates for H3 loops among a dataset of candidates. These can be used to predict the structure of the loop with a higher accuracy than that achieved by any of the presently available methods. The method also has the advantage of being extremely fast and returning a reliable estimate of the model quality. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at http://www.biocomputing.it/H3Loopred/ .
Messih, Mario Abdel; Lepore, Rosalba; Tramontano, Anna(Bioinformatics, Oxford University Press (OUP), 2015-08-06)[Article]
Predicting the structure of protein loops is very challenging, mainly because they are not necessarily subject to strong evolutionary pressure. This implies that, unlike the rest of the protein, standard homology modeling techniques are not very effective in modeling their structure. However, loops are often involved in protein function, hence inferring their structure is important for predicting protein structure as well as function.We describe a method, LoopIng, based on the Random Forest automated learning technique, which, given a target loop, selects a structural template for it from a database of loop candidates. Compared to the most recently available methods, LoopIng is able to achieve similar accuracy for short loops (4-10 residues) and significant enhancements for long loops (11-20 residues). The quality of the predictions is robust to errors that unavoidably affect the stem regions when these are modeled. The method returns a confidence score for the predicted template loops and has the advantage of being very fast (on average: 1 min/loop).firstname.lastname@example.orgSupplementary data are available at Bioinformatics online.
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