Communications of the ACM (CACM for short, not the best sounding acronym around) is the ACM’s flagship magazine. Started in 1957, CACM is handy for keeping up to date on current research being carried out across all topics of computer science and realworld applications. CACM has had an illustrious past with many influential pieces of work and debates started within its pages. These include Hoare’s presentation of the Quicksort algorithm; Rivest, Shamir and Adleman’s description of the first publickey cryptosystem RSA; and Dijkstra’s famous letter against the use of GOTO. In addition to the print edition, which is released monthly, there is a fantastic website (http://cacm.acm. org/) that showcases not only the most recent edition but all previous CACM articles as well, readable online as well as downloadable as a PDF. In addition, the website lets you browse for articles by subject, a handy feature if you want to focus on a particular topic. CACM is really essential reading. Pretty much guaranteed to contain content that is interesting to anyone, it keeps tabs on the latest in computer science. It is a valuable asset for us students, who tend to delve deep into a particular area of CS and forget everything that is happening around us. — Daniel Gooch U ndergraduate research is like a box of chocolates: You never know what kind of project you will get. That being said, there are still a few things you should know to get the most out of the experience.

Communications of the ACM

Structure-based drug design is an iterative process, following cycles of structural biology, computer-aided design, synthetic chemistry and bioassay. In favorable circumstances, this process can lead to the structures of hundreds of protein-ligand crystal structures. In addition, molecular dynamics simulations are increasingly being used to further explore the conformational landscape of these complexes. Currently, methods capable of the analysis of ensembles of crystal structures and MD trajectories are limited and usually rely upon least squares superposition of coordinates. Novel methodologies are described for the analysis of multiple structures of a protein. Statistical approaches that rely upon residue equivalence, but not superposition, are developed. Tasks that can be performed include the identification of hinge regions, allosteric conformational changes and transient binding sites. The approaches are tested on crystal structures of CDK2 and other CMGC protein kinases and a simulation of p38α. Known interaction - conformational change relationships are highlighted but also new ones are revealed. A transient but druggable allosteric pocket in CDK2 is predicted to occur under the CMGC insert. Furthermore, an evolutionarily-conserved conformational link from the location of this pocket, via the αEF-αF loop, to phosphorylation sites on the activation loop is discovered. New methodologies are described and validated for the superimposition independent conformational analysis of large collections of structures or simulation snapshots of the same protein. The methodologies are encoded in a Python package called Polyphony, which is released as open source to accompany this paper [
 http://wrpitt.bitbucket.org/polyphony/
 ].

/pdf/polyphony-superposition-independent-methods-for-ensemble-56r529jjb6.pdf

Polyphony: superposition independent methods for ensemble-based drug discovery

Here a differential geometry (DG) representation of protein backbone is explored on the analyses of protein conformational ensembles. The protein backbone is described by curvature, κ, and torsion, τ, values per residue and we propose 1) a new dissimilarity and protein flexibility measurement and 2) a local conformational clustering method. The methods were applied to Ubiquitin and c-Myb-KIX protein conformational ensembles and results show that κ\τ metric space allows to properly judge protein flexibility by avoiding the superposition problem. The dmax measurement presents equally good or superior results when compared to RMSF, especially for the intrinsically unstructured protein. The clustering method is unique as it relates protein global to local dynamics by providing a global clustering solutions per residue. The methods proposed can be especially useful to the analyses of highly flexible proteins. The software written for the analyses presented here is available at https://github.com/AMarinhoSN/FleXgeo for academic usage only.

/pdf/a-superposition-free-method-for-protein-conformational-1odixi47yb.pdf

A Superposition Free Method for Protein Conformational Ensemble Analyses and Local Clustering Based on a Differential Geometry Representation of Backbone

Chromatin organization is nowadays accessible from population and single cell Hi-C data in the form of large contact matrices. Yet reconstructing the three-dimensional chromatin structure remains challenging and relies on polymer models and numerical simulations to account for these Hi-C data. Here we propose a novel optimization algorithm to identify cross-linker ensemble reproducing the experimental contact matrix. Furthermore, based on the polymer configurations extracted from the optimization procedure, we identify high changing curvature (HCC) regions in the chromatin, detected both in population and in single cell Hi-C, that we also compare to Topological Associated Domains (TADs). We report here that the HCC regions correlate with gene expression and CTCF high density distribution. Interestingly, the HCC region locations are heterogeneous across one cell repeats, revealing cell-to-cell variability. To conclude, HCC regions appear both in single and population Hi-C polymer reconstruction and can provide a possible unit for gene regulation.

/pdf/high-changing-curvature-regions-detect-chromatin-enrichment-192i27fo.pdf

High changing curvature regions detect chromatin enrichment in single cell data

Canonical π-helices are short, relatively unstable secondary structure elements found in proteins. They comprise seven or more residues and are present in 15% of all known protein structures, often in functionally important regions such as ligand- and ion-binding sites. Given their similarity to α-helices, the prediction of π-helices is a challenging task and none of the currently available secondary structure prediction methods tackle it. Here, we present PiPred, a neural network-based tool for predicting π-helices in protein sequences. By performing a rigorous benchmark we show that PiPred can detect π-helices with a per-residue precision of 48% and sensitivity of 46%. Interestingly, some of the α-helices mispredicted by PiPred as π-helices exhibit a geometry characteristic of π-helices. Also, despite being trained only with canonical π-helices, PiPred can identify 6-residue-long α/π-bulges. These observations suggest an even higher effective precision of the method and demonstrate that π-helices, α/π-bulges, and other helical deformations may impose similar constraints on sequences. PiPred is freely accessible at: https://toolkit.tuebingen.mpg.de/#/tools/quick2d . A standalone version is available for download at: https://github.com/labstructbioinf/PiPred , where we also provide the CB6133, CB513, CASP10, and CASP11 datasets, commonly used for training and validation of secondary structure prediction methods, with correctly annotated π-helices.

/pdf/pipred-a-deep-learning-method-for-prediction-of-p-helices-in-4ljcr0zlij.pdf

PiPred - a deep-learning method for prediction of π-helices in protein sequences.

We present ARABESQUE, a new tool for protein structure analysis, which includes structure comparison, generation of annotated structural alignments, and annotated superposition of structures. By combining differential geometry and knot theory, this method produces an accurate analysis of structural conservation in a family of proteins. The annotated alignment and superposed structures are used to char- acterise the local and global structural information content, to refine the sequence alignment and to produce fragments and 3D probability density functions for comparative modelling.

/pdf/arabesque-a-tool-for-protein-structural-comparison-using-k9g2fl90cn.pdf

Arabesque: a tool for protein structural comparison using differential geometry and knot theory

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Arabesque: a tool for protein structural comparison using differential geometry and knot theory