Remote thioredoxin recognition using evolutionary conservation and structural dynamics

Why Are Gly31, Gly33, and Gly35 Highly Conserved in All Fluorescent Proteins?

Green fluorescent protein (GFP)-like fluorescent proteins have been found in more than 120 species. Although the proteins have little sequence identity, Gly31, 33, and 35 are 87, 100, and 95% conserved across all species, respectively. All GFP-like proteins have a β-barrel structure composed of 11 β-sheets, and the 3 conserved glycines are located in the second β-sheet. Molecular dynamics (MD) simulations have shown that mutating one or more of the glycines to alanines most likely does not reduce chromophore formation in correctly folded immature fluorescent proteins. MD and protein characterization of alanine mutants indicate that mutation of the conserved glycines leads to misfolding. Gly31, 33, and 35 are essential to maintain the integrity of the β_1-3 triad that is the last structural element to slot in place in the formation of the canonical fluorescent protein β-barrel. Glycines located in β-sheets may have a similar role in the formation of other non-GFP β-barrels.

A deep learning framework to predict binding preference of RNA constituents on protein surface

Protein-RNA interaction plays important roles in post-transcriptional regulation. However, the task of predicting these interactions given a protein structure is difficult. Here we show that, by leveraging a deep learning model NucleicNet, attributes such as binding preference of RNA backbone constituents and different bases can be predicted from local physicochemical characteristics of protein structure surface. On a diverse set of challenging RNA-binding proteins, including Fem-3-binding-factor 2, Argonaute 2 and Ribonuclease III, NucleicNet can accurately recover interaction modes discovered by structural biology experiments. Furthermore, we show that, without seeing any in vitro or in vivo assay data, NucleicNet can still achieve consistency with experiments, including RNAcompete, Immunoprecipitation Assay, and siRNA Knockdown Benchmark. NucleicNet can thus serve to provide quantitative fitness of RNA sequences for given binding pockets or to predict potential binding pockets and binding RNAs for previously unknown RNA binding proteins.

The Role of Conformational Dynamics and Allostery in the Disease Development of Human Ferritin

Determining the three-dimensional structure of myoglobin, the first solved structure of a protein, fundamentally changed the way protein function was understood. Even more revolutionary was the information that came afterward: protein dynamics play a critical role in biological functions. Therefore, understanding conformational dynamics is crucial to obtaining a more complete picture of protein evolution. We recently analyzed the evolution of different protein families including green fluorescent proteins (GFPs), β-lactamase inhibitors, and nuclear receptors, and we observed that the alteration of conformational dynamics through allosteric regulation leads to functional changes. Moreover, proteome-wide conformational dynamics analysis of more than 100 human proteins showed that mutations occurring at rigid residue positions are more susceptible to disease than flexible residue positions. These studies suggest that disease-associated mutations may impair dynamic allosteric regulations, leading to loss of function. Thus, in this study, we analyzed the conformational dynamics of the wild-type light chain subunit of human ferritin protein along with the neutral and disease forms. We first performed replica exchange molecular dynamics simulations of wild-type and mutants to obtain equilibrated dynamics and then used perturbation response scanning (PRS), where we introduced a random Brownian kick to a position and computed the fluctuation response of the chain using linear response theory. Using this approach, we computed the dynamic flexibility index (DFI) for each position in the chain for the wild-type and the mutants. DFI quantifies the resilience of a position to a perturbation and provides a flexibility/rigidity measurement for a given position in the chain. The DFI analysis reveals that neutral variants and the wild-type exhibit similar flexibility profiles in which experimentally determined functionally critical sites act as hinges in controlling the overall motion. However, disease mutations alter the conformational dynamic profile, making hinges more loose (i.e., softening the hinges), thus impairing the allosterically regulated dynamics.

Knowledge-based fragment binding prediction

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.

High precision prediction of functional sites in protein structures

We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta.

Structural Consequences of Chromophore Formation and Exploration of Conserved Lid Residues amongst Naturally Occurring Fluorescent Proteins

Computational methods were used to generate the lowest energy conformations of the immature precyclized forms of the 28 naturally occurring GFP-like proteins deposited in the pdb. In all 28 GFP-like proteins, the beta-barrel contracts upon chromophore formation and becomes more rigid. Our prior analysis of over 260 distinct naturally occurring GFP-like proteins revealed that most of the conserved residues are located in the top and bottom of the barrel in the turns between the β-sheets.(1) Structural analyses, molecular dynamics simulations and the Anisotropic Network Model were used to explore the role of these conserved lid residues as possible folding nuclei. Our results are internally consistent and show that the conserved residues in the top and bottom lids undergo relatively less translational movement than other lid residues, and a number of these residues may play an important role as hinges or folding nuclei in the fluorescent proteins.

CEP proteins: the knights of centrosome dynasty

Centrosome forms the backbone of cell cycle progression mechanism. Recent debates have occurred regarding the essentiality of centrosome in cell cycle regulation. CEP family protein is the active component of centrosome and plays a vital role in centriole biogenesis and cell cycle progression control. A total of 31 proteins have been categorized into CEP family protein category and many more are under candidate evaluation. Furthermore, by the recent advancements in genomics and proteomics researches, several new CEP proteins have also been characterized. Here we have summarized the importance of CEP family proteins and their regulation mechanism involved in proper cell cycle progression. Further, we have reviewed the detailed molecular mechanism behind the associated pathological phenotypes and the possible therapeutic approaches. Proteins such as CEP57, CEP63, CEP152, CEP164, and CEP215 have been extensively studied with a detailed description of their molecular mechanisms, which are among the primary targets for drug discovery. Moreover, CEP27, CEP55, CEP70, CEP110, CEP120, CEP135, CEP192, CEP250, CEP290, and CEP350 also seem promising for future drug discovery approaches. Since the overview implicates that the overall researches on CEP proteins are not yet able to present significant details required for effective therapeutics development, thus, it is timely to discuss the importance of future investigations in this field.

Bioinformatics and variability in drug response: a protein structural perspective

Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk-benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein-drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.

Sequence evolution correlates with structural dynamics

Biochemical activity and core stability are essential properties of proteins, maintained usually by conserved amino acids. Structural dynamics emerged in recent years as another essential aspect of protein functionality. Structural dynamics enable the adaptation of the protein to binding substrates and to undergo allosteric transitions, while maintaining the native fold. Key residues that mediate structural dynamics would thus be expected to be conserved or exhibit coevolutionary patterns at least. Yet, the correlation between sequence evolution and structural dynamics is yet to be established. With recent advances in efficient characterization of structural dynamics, we are now in a position to perform a systematic analysis. In the present study, a set of 34 enzymes representing various folds and functional classes is analyzed using information theory and elastic network models. Our analysis shows that the structural regions distinguished by their coevolution propensity as well as high mobility are predisposed to serve as substrate recognition sites, whereas residues acting as global hinges during collective dynamics are often supported by conserved residues. We propose a mobility scale for different types of amino acids, which tends to vary inversely with amino acid conservation. Our findings suggest the balance between physical adaptability (enabled by structure-encoded motions) and chemical specificity (conferred by correlated amino acid substitutions) underlies the selection of a relatively small set of versatile folds by proteins.

Using multiple microenvironments to find similar ligand-binding sites: application to kinase inhibitor binding

The recognition of cryptic small-molecular binding sites in protein structures is important for understanding off-target side effects and for recognizing potential new indications for existing drugs. Current methods focus on the geometry and detailed chemical interactions within putative binding pockets, but may not recognize distant similarities where dynamics or modified interactions allow one ligand to bind apparently divergent binding pockets. In this paper, we introduce an algorithm that seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared microenvironments. The method has relatively weak geometric requirements (to allow for conformational change or dynamics in both the ligand and the pocket) and uses multiple biophysical and biochemical measures to characterize the microenvironments (to allow for diverse modes of ligand binding). We term the algorithm PocketFEATURE, since it focuses on pockets using the FEATURE system for characterizing microenvironments. We validate PocketFEATURE first by showing that it can better discriminate sites that bind similar ligands from those that do not, and by showing that we can recognize FAD-binding sites on a proteome scale with Area Under the Curve (AUC) of 92%. We then apply PocketFEATURE to evolutionarily distant kinases, for which the method recognizes several proven distant relationships, and predicts unexpected shared ligand binding. Using experimental data from ChEMBL and Ambit, we show that at high significance level, 40 kinase pairs are predicted to share ligands. Some of these pairs offer new opportunities for inhibiting two proteins in a single pathway.

Small molecule drugs may interact with many proteins. Some of these interactions may cause unexpected effects, including side effects or potentially useful therapeutic effects. One way to predict these effects is to analyze the three-dimensional structure of target proteins, and identify new binding sites for small molecule drugs. Several methods have been proposed for predicting new binding sites, relying on geometric and functional complementarity of the sites and the small molecules. In this paper, we report on a new method for identifying novel protein-drug interactions by analyzing the similarity between binding sites in proteins. The method has relatively weak geometric requirements and allows for conformational change or dynamics in both the ligand and protein. Our results show that geometric flexibility is useful for effectively comparing sites. We have applied the method to evolutionarily distant kinases, and find unexpected shared inhibitor binding. Our results may be valuable for drug repurposing in order to find novel uses for existing kinase inhibitors.