A superposition free method for protein conformational ensemble analyses and local clustering based on a differential geometry representation of backbone

Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins

Selectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family.

Role of Q675H Mutation in Improving SARS-CoV-2 Spike Interaction with the Furin Binding Pocket

Genotype screening was implemented in Italy and showed a significant prevalence of new SARS-CoV-2 mutants carrying Q675H mutation, near the furin cleavage site of spike protein. Currently, this mutation, which is expressed on different SARS-CoV-2 lineages circulating worldwide, has not been thoughtfully investigated. Therefore, we performed phylogenetic and biocomputational analysis to better understand SARS-CoV-2 Q675H mutants’ evolutionary relationships with other circulating lineages and Q675H function in its molecular context. Our studies reveal that Q675H spike mutation is the result of parallel evolution because it arose independently in separate evolutionary clades. In silico data show that the Q675H mutation gives rise to a hydrogen-bonds network in the spike polar region. This results in an optimized directionality of arginine residues involved in interaction of spike with the furin binding pocket, thus improving proteolytic exposure of the viral protein. Furin was predicted to have a greater affinity for Q675H than Q675 substrate conformations. As a consequence, Q675H mutation could confer a fitness advantage to SARS-CoV-2 by promoting a more efficient viral entry. Interestingly, here we have shown that Q675H spike mutation is documented in all the VOCs. This finding highlights that VOCs are still evolving to enhance viral fitness and to adapt to the human host. At the same time, it may suggest Q675H spike mutation involvement in SARS-CoV-2 evolution.

New technologies to analyse protein function: an intrinsic disorder perspective

Functions of intrinsically disordered proteins do not require structure. Such structure-independent functionality has melted away the classic rigid "lock and key" representation of structure-function relationships in proteins, opening a new page in protein science, where molten keys operate on melted locks and where conformational flexibility and intrinsic disorder, structural plasticity and extreme malleability, multifunctionality and binding promiscuity represent a new-fangled reality. Analysis and understanding of this new reality require novel tools, and some of the techniques elaborated for the examination of intrinsically disordered protein functions are outlined in this review.

Interactions between motor domains in kinesin-14 Ncd - a molecular dynamics study

Minus-end directed, non-processive kinesin-14 Ncd is a dimeric protein with C-terminally located motor domains (heads). Generation of the power-stroke by Ncd consists of a lever-like rotation of a long superhelical 'stalk' segment while one of the kinesin's heads is bound to the microtubule. The last ∼30 amino acids of Ncd head play a crucial but still poorly understood role in this process. Here, we used accelerated molecular dynamics simulations to explore the conformational dynamics of several systems built upon two crystal structures of Ncd, the asymmetrical T436S mutant in pre-stroke/post-stroke conformations of two partner subunits and the symmetrical wild-type protein in pre-stroke conformation of both subunits. The results revealed a new conformational state forming following the inward motion of the subunits and stabilized with several hydrogen bonds to residues located on the border or within the C-terminal linker, i.e. a modeled extension of the C-terminus by residues 675-683. Forming of this new, compact Ncd conformation critically depends on the length of the C-terminus extending to at least residue 681. Moreover, the associative motion leading to the compact conformation is accompanied by a partial lateral rotation of the stalk. We propose that the stable compact conformation of Ncd may represent an initial state of the working stroke.

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

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.