PDF | Structural Bioinformatics is one of the hot spots of interdisciplinary sciences and obtained amazing advances in recent years. The first chapter overviews. Structural Bioinformatics. K. Anton Feenstra. Sanne Abeln. Centre for Integrative Bioinformatics (IBIVU), and. Department of Computer Science. Structural bioinformatics / edited by Philip E. Bourne, Helge. Weissig. p. ; cm. – ( Methods PDF versions of the PDB newsletter dating back to.

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Structural Bioinformatics is an interdisciplinary field that deals with the three dimensional structures of biomolecules. It attempts to model and discover the basic. Structural bioinformatics is computer aided structural biology! • Characterizes biomolecules and their assembles at the molecular & atomic level. Q. Why should . Structural Bioinformatics Prof. Haim J. Wolfson. 1. Lecture 1 - Introduction to. Structural Bioinformatics. Motivation and Basics of Protein. Structure.

Watson, Gail J. Bartlett, and Janet M. Jonikas, Alain Laederach, and Russ B. Burley, Steven C.

Structural Bioinformatics: Practical Guide

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Structural Bioinformatics was the first major effort to show the application of the principles and basic knowledge of the larger field of bioinformatics to questions focusing on macromolecular structure, such as the prediction of protein structure and how proteins carry out cellular functions, and how the application of bioinformatics to these life science issues can improve healthcare by accelerating drug discovery and development.

Designed primarily as a reference, the first edition nevertheless saw widespread use as a textbook in graduate and undergraduate university courses dealing with the theories and associated algorithms, resources, and tools used in the analysis, prediction, and theoretical underpinnings of DNA, RNA, and proteins. Praise for the previous edition: Table of contents Foreword. Scheeff and J. Lynn Fink. Andersen and Burkhard Rost. Baker and J. Andrew McCammon. Ponomarenko and Marc H.

Observation selection bias in contact prediction and its implications for structural bioinformatics

Reviews "Offering a detailed coverage for practitioners but remaining accessible to the novice, Structural Bioinformatics, Second Edition is a valuable and excellent textbook for readers in the bioinformatics and advanced biology fields, and on the best way to become a classic reference for all interested parties educators, researchers and graduate students. Extra Bookcast Click here to listen to the editor Philip E.

Bourne and watch a bookcast. What's New A compilation of chapters contributed from leading experts in the field. There are no other books that directly and so comprehensively address the field in this way. Updates include new frontiers for the field of Structural Bioinformatics, such as understanding membrane proteins, protein motion, dynamics, and evolution, not well covered by other texts.

A powerful textbook from which advanced undergraduate or graduate courses in structural bioinformatics can be constructed. See for example http: Where structural bioinformatics has had significant impact on other fields such as immunology and drug discovery are highlighted.

This effect is strong, with some structural bioinformatics tools showing a clear correlation between the number of homologous sequences retrieved by the alignment algorithm and the reliability of their predictions 15 , 25 , 37 , Fields in which this effect has been observed include, but are not limited to, functional characterization of linear motifs 39 , domain boundaries identification 40 , DNA-binding sites prediction 41 , disulfide bonds connectivity prediction 15 , fold recognition 42 and Contact Prediction 25 , All bioinformatics tools developed to address protein structure-related tasks share the same, crucial, characteristic: they need a validation procedure based on experimentally determined data to evaluate their performances.

The underlying assumption is that if a method works well for the proteins in the validation set, it will also work for ones with unknown structure.

Integrating protein annotations for the in silico prioritization of putative drug target proteins

In other words, this procedure is reliable only if the validation data is representative of the entire population of protein sequences, with no significant difference between the subset of experimentally investigated proteins and all non-investigated ones. The intrinsic nature of this structure-based validation in structural bioinformatics could be a major cause for observation selection bias, where particular properties of an object are correlated with its probability to be sampled.

In this work we show that observation selection bias can indeed skew the performance of structural bioinformatics methods. First, we show a striking difference between the availability of homologs for proteins with a PDB structure and for proteins where only the Uniprot sequence is available, which translates to lower overall NEFF scores 43 , a score equal to the average number of different amino acids in each column of the MSA, and lower average residue entropies for the latter sequences.

The performance of structural bioinformatics methods that i are trained on experimental structural data and ii use evolutionary information to improve their prediction is therefore likely over-estimated with respect to real case applications.

Structural Bioinformatics, 2nd Edition

We show that this is indeed the case in the Contact Prediction CP field, where protein structures are predicted by inferring inter-residue contacts. The CP field fits criteria i and ii , with a well documented correlation between the number of homologous sequences available and the prediction performances, so making the observation selection bias immediately and directly relevant 25 , 37 , Moreover, the widely adopted use of unsupervised prediction methods in this field facilitates the fair evaluation of the prediction in function of different datasets, without the confounding overfitting effects of supervised methods.

Overall, our findings question the de facto applicability of structural bioinformatics tools that fit the two criteria on real cases, i. This is essential not only to understand the reliability of the results, but also to avoid long-term negative effects on structural bioinformatics research: the necessity to boost the performances of a tool in order to achieve a publication could lead to a positive selection of methods that take advantage from information that is not available in real case applications.

Figure 1: Overview of the analysis. There is a significant difference in the number of homologs that can be retrieved for a protein with and without a solved structure. This can lead to an overestimation of the performances of methods that use this kind of information, as we show for contact prediction, where this effect is very strong.

Results and Discussion Investigating the relationship between retrieved homologous sequences and the availability of 3D structures We first evaluated the amount of homologous sequences that can be retrieved for proteins with known or unknown three dimensional structure.

From the resulting set of proteins we randomly selected sequences.

The distribution of the number of retrieved homologous sequences Fig. While it is well known that the sequences in the PSICOV dataset tend to have more homologs, our results show that this difference is more fundamental and concerns a discrepancy in homologs between proteins from Uniprot and proteins with a solved structure in the PDB.

This difference affects every dataset based on a random selection of protein structures.

The results are shown in Supplementary Figure 1. Full size image To ensure that this effect is not due an uncontrolled variable that affects the capability of the alignment tools to retrieve homologous sequences, we investigated several factors. A more biophysical reason could be that the alignment algorithms are less able to deal with fully or partially disordered proteins, which are also difficult to study with structural biology methods such as X-ray diffraction and would therefore be much less represented in the STRUCT dataset.

To verify if a simple organism-based filter could remove all possible biases, we replicated the analysis shown in Fig. These results are striking, but the number of available sequences may not be the best criterium for evaluating the difference between the datasets, as alignment methods may retrieve very similar sequences and provide a redundant collection of homologs.

A higher number of homologs would then not necessarily correspond to a higher information content. We also calculated the NEFF score, which relates the average sequence variation within each MSA, and ranges from 1, if all the sequences are identical, to 20, if there is complete variability in every column.

Finally, Fig.

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More details are available in Supplementary Figures 8 and The relevance of the homologs availability in Structural Bioinformatics: the Contact Prediction case The relevance of the availability and quality of MSAs for prediction performances in structural bioinformatics is well documented 5 , 7 , 8 , 15 , 17 , 31 , 32 , 33 and it is particularly evident in CP, both in terms of the number of available homologs 38 and of information content NEFF These results question the consistency of the accuracy that CP methods claim, since their published performances are calculated on protein datasets that are significantly enriched in number of available homologs compared to real application cases.

We selected PSICOV because it is a landmark method in this field and CCMpred because is the most recent implementation of a popular statistical mechanics based method Figure 4 shows the median precision scores PPV for the best L predicted contacts with sequence separation greater than 4 residues, where L is equal to the sequence length of each protein see also Supplementary Table 1 for the mean precisions. The performance, as expected, improves when increasing the number of iterations for jackhmmer, meaning more homologs are collected.

The shaded area indicates for each iteration the data between the 40th and the 60th percentile and between the 25th and 75th percentile. This indicates that NOUMENON does not penalize the scores of these predictions more than what is expected solely due to the reduced number of homologs available.

Conclusions Many structural bioinformatics methods that predict structural characteristics from protein sequence validate their performance on known protein structures and use evolutionary information in to boost prediction performance. We show here that proteins for which experimentally determined structures from the PDB exist have significantly more homologous sequences available, with a higher information content in the corresponding MSAs, than typical proteins from Uniprot without characterised structures.

This represents an observation selection bias that inflates prediction performance because more homologs are available for exactly those proteins that constitute the validation sets: the evolutionary information available for validated proteins differ from the real case applications for which bioinformatics methods intend to provide useful annotations. We demonstrate this observation selection bias with contact prediction CP methods, for which the dependence between performances and number of homologs is particularly pronounced; the datasets used for the validation of CP methods are even more enriched with homologs in comparison to the general distribution of homologs found in the PDB.Here we show that there is a substantial observational selection bias in this approach: the predictions are validated on proteins with known structures from the PDB, but exactly for those proteins significantly more homologs are available compared to less studied sequences randomly extracted from Uniprot.

First, we show a striking difference between the availability of homologs for proteins with a PDB structure and for proteins where only the Uniprot sequence is available, which translates to lower overall NEFF scores 43 , a score equal to the average number of different amino acids in each column of the MSA, and lower average residue entropies for the latter sequences.

A more biophysical reason could be that the alignment algorithms are less able to deal with fully or partially disordered proteins, which are also difficult to study with structural biology methods such as X-ray diffraction and would therefore be much less represented in the STRUCT dataset. About this Course 22, recent views Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists.

Reviews " This course explores bioinformatics data resources and tools for the interpretation and exploitation of biomacromolecular structures.

In order to properly assess the performance of CP methods on real case applications, the homolog distributions have to reflect the general situation found in Uniprot.

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