Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

Motivation: In any macromolecular polyprotic system - for example protein, DNA or RNA - the isoelectric point - commonly referred to as the pI - can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge - and thus the electrophoretic mobility - of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: yperez@ebi.ac.uk Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.

Exploiting heterogeneous scientific literature networks to combat ranking bias:evidence from the computational linguistics area

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. MutualRank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that MutualRank greatly outperforms the state-of-the-art competitors, including PageRank, HITS, CoRank, Future Rank, and P-Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.

An investigation of primary human cell sources and clinical scaffolds for articular cartilage repair

Damage to articular cartilage of the knee can be debilitating because it lacks the capacity to repair itself and can progress to degenerative disorders such as osteoarthritis. The current gold standard for treating cartilage defects is autologous chondrocyte implantation (ACI). However, one of the major limitations of ACI is the use of chondrocytes, which dedifferentiate when grown in vitro and lose their phenotype. It is not clear whether the dedifferentiated chondrocytes can fully redifferentiate upon in vivo transplantation. Studies have suggested that undifferentiated mesenchymal stem or stromal cells (MSCs) from bone marrow (BM) and adipose tissue (AT) can undergo chondrogenic differentiation. Therefore, the main aim of this thesis was to examine BM and AT as a cell source for chondrogenesis using clinical scaffolds. Initially, freshly isolated cells were compared with culture expanded MSCs from BM and AT in Chondro-Gide®, Alpha Chondro Shield® and Hyalofast™. MSCs were shown to grow better in the three scaffolds compared to freshly isolated cells. BM MSCs in Chondro-Gide® were shown to have increased deposition of cartilage specific extracellular matrix (ECM) compared to AT MSCs. Further, this thesis has sought to examine whether CD271 selected MSCs from AT were more chondrogenic than MSCs selected on the basis of plastic adherence (PA). It was shown that CD271+MSCs may have superior chondrogenic properties in vitro and in vivo in terms of ECM deposition. The repair tissue seen after CD271+MSC transplantation combined with Alpha Chondro Shield® was also less vascularised than that seen after transplantation with PA MSCs in the same scaffold, suggesting antiangiogenic activity. Since articular cartilage is an avascular tissue, CD271+MSCs may be a better suited cell type compared to the PA MSCs. Hence, this study has increased the current understanding of how different cell-scaffold combinations may best be used to promote articular cartilage repair.

Quantising contribution effort in online communities

We describe the Joint Effort-Topic (JET) model and the Author Joint Effort-Topic (aJET) model that estimate the effort required for users to contribute on different topics. We propose to learn word-level effort taking into account term preference over time and use it to set the priors of our models. Since there is no gold standard which can be easily built, we evaluate them by measuring their abilities to validate expected behaviours such as correlations between user contributions and the associated effort.