Implications of AlphaFold2 for crystallographic phasing by molecular replacement

The AlphaFold2 results in the 14th edition of Critical Assessment of Structure Prediction (CASP14) showed that accurate (low root-mean-square deviation) in silico models of protein structure domains are on the horizon, whether or not the protein is related to known structures through high-coverage sequence similarity. As highly accurate models become available, generated by harnessing the power of correlated mutations and deep learning, one of the aspects of structural biology to be impacted will be methods of phasing in crystallography. Here, the data from CASP14 are used to explore the prospects for changes in phasing methods, and in particular to explore the prospects for molecular-replacement phasing using in silico models.

Discovery of a Kojibiose Hydrolase by Analysis of Specificity-Determining Correlated Positions in Glycoside Hydrolase Family 65

The Glycoside Hydrolase Family 65 (GH65) is an enzyme family of inverting a-glucoside phosphorylases and hydrolases that currently contains 10 characterized enzyme specificities. However, its sequence diversity has never been studied in detail. Here, an in-silico analysis of correlated mutations was performed, revealing specificity-determining positions that facilitate annotation of the family’s phylogenetic tree. By searching these positions for amino acid motifs that do not match those found in previously characterized enzymes from GH65, several clades that may harbor new functions could be identified. Three enzymes from across these regions were expressed in E. coli and their substrate profile was mapped. One of those enzymes, originating from the bacterium Mucilaginibacter mallensis, was found to hydrolyze kojibiose and a-1,2-oligoglucans with high specificity. We propose kojibiose glucohydrolase as the systematic name and kojibiose hydrolase or kojibiase as the short name for this new enzyme. This work illustrates a convenient strategy for mapping the natural diversity of enzyme families and smartly mining the ever-growing number of available sequences in the quest for novel specificities.

Saturation Mutagenesis of the Transmembrane Region of HokC in Escherichia coli Reveals Its High Tolerance to Mutations

Cells adapt to different stress conditions, such as the antibiotics presence. This adaptation sometimes is achieved by changing relevant protein positions, of which the mutability is limited by structural constrains. Understanding the basis of these constrains represent an important challenge for both basic science and potential biotechnological applications. To study these constraints, we performed a systematic saturation mutagenesis of the transmembrane region of HokC, a toxin used by Escherichia coli to control its own population, and observed that 92% of single-point mutations are tolerated and that all the non-tolerated mutations have compensatory mutations that reverse their effect. We provide experimental evidence that HokC accumulates multiple compensatory mutations that are found as correlated mutations in the HokC family multiple sequence alignment. In agreement with these observations, transmembrane proteins show higher probability to present correlated mutations and are less densely packed locally than globular proteins; previous mutagenesis results on transmembrane proteins further support our observations on the high tolerability to mutations of transmembrane regions of proteins. Thus, our experimental results reveal the HokC transmembrane region high tolerance to loss-of-function mutations that is associated with low sequence conservation and high rate of correlated mutations in the HokC family sequences alignment, which are features shared with other transmembrane proteins.

Conservation and Identity Selection of Cationic Residues Flanking the Hydrophobic Regions in Intermediate Filament Superfamily

The interplay between the hydrophobic interactions generated by the nonpolar region and the proximal functional groups within nanometers of the nonpolar region offers a promising strategy to manipulate the intermolecular hydrophobic attractions in an artificial molecule system, but the outcomes of such modulations in the building of a native protein architecture remain unclear. Here we focus on the intermediate filament (IF) coiled-coil superfamily to assess the conservation of positively charged residue identity via a biostatistical approach. By screening the disease-correlated mutations throughout the IF superfamily, 10 distinct hotspots where a cation-to-cation substitution is associated with a pathogenic syndrome have been identified. The analysis of the local chemical context surrounding the hotspots revealed that the cationic diversity depends on their separation distance to the hydrophobic domain. The nearby cationic residues flanking the hydrophobic domain of a helix (separation <1 nm) are relatively conserved in evolution. In contrast, the cationic residues that are not adjacent to the hydrophobic domain (separation >1 nm) tolerate higher levels of variation and replaceability. We attribute this bias in the conservation degree of the cationic residue identity to reflect the interplay between the proximal cations and the hydrophobic interactions.

Possible Implications of AlphaFold2 for Crystallographic Phasing by Molecular Replacement

The AlphaFold2 results in the 14th edition of Critical Assessment of Structure Prediction (CASP14) showed that accurate (low root-mean-square deviation) in silico models of protein structure domains are on the horizon, whether or not the protein is related to known structures through high-coverage sequence similarity. As highly accurate models become available, generated by harnessing the power of correlated mutations and deep learning, one of the aspects of structural biology to be impacted will be methods of phasing in crystallography. We here use the data from CASP14 to explore the prospect for changes in phasing methods, and in particular to explore the prospects for molecular replacement phasing using in silico models.

Rosetta design with co-evolutionary information retains protein function

Computational protein design has the ambitious goal of crafting novel proteins that address challenges in biology and medicine. To overcome these challenges, the computational protein modeling suite Rosetta has been tailored to address various protein design tasks. Recently, statistical methods have been developed that identify correlated mutations between residues in a multiple sequence alignment of homologous proteins. These subtle inter-dependencies in the occupancy of residue positions throughout evolution are crucial for protein function, but we found that three current Rosetta design approaches fail to recover these co-evolutionary couplings. Thus, we developed the Rosetta method ResCue (residue-coupling enhanced) that leverages co-evolutionary information to favor sequences which recapitulate correlated mutations, as observed in nature. To assess the protocols via recapitulation designs, we compiled a benchmark of ten proteins each represented by two, structurally diverse states. We could demonstrate that ResCue designed sequences with an average sequence recovery rate of 70%, whereas three other protocols reached not more than 50%, on average. Our approach had higher recovery rates also for functionally important residues, which were studied in detail. This improvement has only a minor negative effect on the fitness of the designed sequences as assessed by Rosetta energy. In conclusion, our findings support the idea that informing protocols with co-evolutionary signals helps to design stable and native-like proteins that are compatible with the different conformational states required for a complex function.

Coevolution study of tau and a-synuclein suggests a connection between their normal interaction in neurons and the Parkinson's disease-associated mutation A53T

Alpha-synuclein lies at the center of Parkinson’s disease etiology, and polymorphisms in the gene for the microtubule-associated protein tau are risk factors for getting the disease. Tau and a-synuclein interact in vitro, and a-synuclein can also compete with tau binding to microtubules. To test whether these interactions might be part of their natural biological functions, a correlated mutation analysis was performed between tau and a-synuclein, looking for evidence of coevolution. For comparison, analyses were also performed between tau and b- and g-synuclein. In addition, analyses were performed between tau and the synuclein proteins and the neuronal tubulin proteins. Potential correlated mutations were detected between tau and a-synuclein, one involving an a-synuclein residue known to interact with tau in vitro, Asn122, and others involving the Parkinson’s disease-associated mutation A53T. No significant correlated mutations were seen between tau and b- and g-synuclein. Tau showed potential correlated mutations with the neuron-specific bIII-tubulin protein, encoded by the TUBB3 gene. No convincing correlated mutations were seen between the synuclein and tubulin proteins, with the possible exception of b-synuclein with bIVa-tubulin, encoded by the TUBB4A gene. While the correlated mutations between tau and a-synuclein suggest the two proteins have coevolved, additional study will be needed to confirm that their interaction is part of their normal biological function in cells.

The Impact of Gag Non-Cleavage Site Mutations on HIV-1 Viral Fitness from Integrative Modelling and Simulations

The high mutation rate in retroviruses is one of the leading causes of drug resistance. In human immunodeficiency virus type 1 (HIV 1), synergistic mutations in its protease and the protease substrate , the Group-specific antigen (Gag) polyprotein, work together to confer drug resistance against protease inhibitors and compensate the mutations affecting viral fitness. Some Gag mutations can restore Gag-protease binding, yet most Gag-protease correlated mutations occur outside of the Gag cleavage site. To investigate the molecular basis for this, we now report multiscale modelling approaches to investigate various sequentially cleaved Gag products in the context of clinically relevant mutations that occur outside of the cleavage sites, including simulations of the largest Gag proteolytic product in its viral membrane-bound state. We found that some mutations, such as G123E and H219Q, involve direct interaction with cleavage site residues to influence their local environment, while certain mutations in the matrix domain lead to the enrichment of lipids important for Gag targeting and assembly. Collectively, our results reveal why non-cleavage site mutations have far-reaching implications outside of Gag proteolysis, with important consequences for drugging Gag maturation intermediates and tackling protease inhibitor resistance.

Protein Inter-Residue Contacts Prediction: Methods, Performances and Applications

Background:Protein inter-residue contacts prediction play an important role in the field of protein structure and function research. As a low-dimensional representation of protein tertiary structure, protein inter-residue contacts could greatly help de novo protein structure prediction methods to reduce the conformational search space. Over the past two decades, various methods have been developed for protein inter-residue contacts prediction.Objective:We provide a comprehensive and systematic review of protein inter-residue contacts prediction methods.Results:Protein inter-residue contacts prediction methods are roughly classified into five categories: correlated mutations methods, machine-learning methods, fusion methods, templatebased methods and 3D model-based methods. In this paper, firstly we describe the common definition of protein inter-residue contacts and show the typical application of protein inter-residue contacts. Then, we present a comprehensive review of the three main categories for protein interresidue contacts prediction: correlated mutations methods, machine-learning methods and fusion methods. Besides, we analyze the constraints for each category. Furthermore, we compare several representative methods on the CASP11 dataset and discuss performances of these methods in detail.Conclusion:Correlated mutations methods achieve better performances for long-range contacts, while the machine-learning method performs well for short-range contacts. Fusion methods could take advantage of the machine-learning and correlated mutations methods. Employing more effective fusion strategy could be helpful to further improve the performances of fusion methods.