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1. The goddard and saturn genes are essential for Drosophila male fertility and may have arisen de novo

   https://doi.org/10.1093/molbev/msx057  

   Gubala, Anna M.; Schmitz, Jonathan F.; Kearns, Michael J.; Vinh, Tery T.; Bornberg-Bauer, Erich; Wolfner, Mariana F.; Findlay, Geoffrey D. (May 2017, Molecular biology and evolution)

   New genes arise through a variety of mechanisms, including the duplication of existing genes and the de novo birth of genes from noncoding DNA sequences. While there are numerous examples of duplicated genes with important func- tional roles, the functions of de novo genes remain largely unexplored. Many newly evolved genes are expressed in the male reproductive tract, suggesting that these evolutionary innovations may provide advantages to males experiencing sexual selection. Using testis-specific RNA interference, we screened 11 putative de novo genes in Drosophila mela- nogaster for effects on male fertility and identified two, goddard and saturn, that are essential for spermatogenesis and sperm function. Goddard knockdown (KD) males fail to produce mature sperm, while saturn KD males produce few sperm, and these function inefficiently once transferred to females. Consistent with a de novo origin, both genes are identifiable only in Drosophila and are predicted to encode proteins with no sequence similarity to any annotated protein. However, since high levels of divergence prevented the unambiguous identification of the noncoding sequences from which each gene arose, we consider goddard and saturn to be putative de novo genes. Within Drosophila, both genes have been lost in certain lineages, but show conserved, male-specific patterns of expression in the species in which they are found. Goddard is consistently found in single-copy and evolves under purifying selection. In contrast, saturn has diversified through gene duplication and positive selection. These data suggest that de novo genes can acquire essential roles in male reproduction. 

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2. Artificial intelligence advances for de novo molecular structure modeling in cryo‐electron microscopy

   https://doi.org/10.1002/wcms.1542  

   Si, Dong; Nakamura, Andrew; Tang, Runbang; Guan, Haowen; Hou, Jie; Firozi, Ammaar; Cao, Renzhi; Hippe, Kyle; Zhao, Minglei (May 2021, WIREs Computational Molecular Science)

   Abstract Cryo‐electron microscopy (cryo‐EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo‐EM has been drastically improved to generate high‐resolution three‐dimensional maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo‐EM model building approach is template‐based homology modeling. Manual de novo modeling is very time‐consuming when no template model is found in the database. In recent years, de novo cryo‐EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top‐performing methods in macromolecular structure modeling. DL‐based de novo cryo‐EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, we systematically review the representative ML/DL‐based de novo cryo‐EM modeling methods. Their significances are discussed from both practical and methodological viewpoints. We also briefly describe the background of cryo‐EM data processing workflow. Overall, this review provides an introductory guide to modern research on artificial intelligence for de novo molecular structure modeling and future directions in this emerging field. This article is categorized under:Structure and Mechanism > Molecular StructuresStructure and Mechanism > Computational Biochemistry and BiophysicsData Science > Artificial Intelligence/Machine Learning 

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3. Adaptive Stochastic Optimization to Improve Protein Conformation Sampling

   https://doi.org/10.1109/TCBB.2021.3134103  

   Zaman, Ahmed Bin; Inan, Toki Tahmid; De Jong, Kenneth; Shehu, Amarda (January 2021, IEEE/ACM Transactions on Computational Biology and Bioinformatics)

   We have long known that characterizing protein structures structure is key to understanding protein function. Computational approaches have largely addressed a narrow formulation of the problem, seeking to compute one native structure from an amino-acid sequence. Now AlphaFold2 promises to reveal a high-quality native structure for possibly many proteins. However, researchers over the years have argued for broadening our view to account for the multiplicity of native structures. We now know that many protein molecules switch between different structures to regulate interactions with molecular partners in the cell. Elucidating such structures de novo is exceptionally difficult, as it requires exploration of possibly a very large structure space in search of competing, near-optimal structures. Here we report on a novel stochastic optimization method capable of revealing very different structures for a given protein from knowledge of its amino-acid sequence. The method leverages evolutionary search techniques and adapts its exploration of the search space to balance between exploration and exploitation in the presence of a computational budget. In addition to demonstrating the utility of this method for identifying multiple native structures, we additionally provide a benchmark dataset for researchers to continue work on this problem. 

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4. From peptides to proteins: coiled-coil tetramers to single-chain 4-helix bundles

   https://doi.org/10.1039/d2sc04479j  

   Naudin, Elise A.; Albanese, Katherine I.; Smith, Abigail J.; Mylemans, Bram; Baker, Emily G.; Weiner, Orion D.; Andrews, David M.; Tigue, Natalie; Savery, Nigel J.; Woolfson, Derek N. (October 2022, Chemical Science)

   The design of completely synthetic proteins from first principles— de novo protein design—is challenging. This is because, despite recent advances in computational protein–structure prediction and design, we do not understand fully the sequence-to-structure relationships for protein folding, assembly, and stabilization. Antiparallel 4-helix bundles are amongst the most studied scaffolds for de novo protein design. We set out to re-examine this target, and to determine clear sequence-to-structure relationships, or design rules, for the structure. Our aim was to determine a common and robust sequence background for designing multiple de novo 4-helix bundles. In turn, this could be used in chemical and synthetic biology to direct protein–protein interactions and as scaffolds for functional protein design. Our approach starts by analyzing known antiparallel 4-helix coiled-coil structures to deduce design rules. In terms of the heptad repeat, abcdefg — i.e. , the sequence signature of many helical bundles—the key features that we identify are: a = Leu, d = Ile, e = Ala, g = Gln, and the use of complementary charged residues at b and c. Next, we implement these rules in the rational design of synthetic peptides to form antiparallel homo- and heterotetramers. Finally, we use the sequence of the homotetramer to derive in one step a single-chain 4-helix-bundle protein for recombinant production in E. coli . All of the assembled designs are confirmed in aqueous solution using biophysical methods, and ultimately by determining high-resolution X-ray crystal structures. Our route from peptides to proteins provides an understanding of the role of each residue in each design. 

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5. Allosteric cooperation in a de novo-designed two-domain protein

   https://doi.org/10.1073/pnas.2017062117  

   Pirro, Fabio; Schmidt, Nathan; Lincoff, James; Widel, Zachary X.; Polizzi, Nicholas F.; Liu, Lijun; Therien, Michael J.; Grabe, Michael; Chino, Marco; Lombardi, Angela; et al (December 2020, Proceedings of the National Academy of Sciences)

   null (Ed.)

   We describe the de novo design of an allosterically regulated protein, which comprises two tightly coupled domains. One domain is based on the DF (Due Ferri in Italian or two-iron in English) family of de novo proteins, which have a diiron cofactor that catalyzes a phenol oxidase reaction, while the second domain is based on PS1 (Porphyrin-binding Sequence), which binds a synthetic Zn-porphyrin (ZnP). The binding of ZnP to the original PS1 protein induces changes in structure and dynamics, which we expected to influence the catalytic rate of a fused DF domain when appropriately coupled. Both DF and PS1 are four-helix bundles, but they have distinct bundle architectures. To achieve tight coupling between the domains, they were connected by four helical linkers using a computational method to discover the most designable connections capable of spanning the two architectures. The resulting protein, DFP1 (Due Ferri Porphyrin), bound the two cofactors in the expected manner. The crystal structure of fully reconstituted DFP1 was also in excellent agreement with the design, and it showed the ZnP cofactor bound over 12 Å from the dimetal center. Next, a substrate-binding cleft leading to the diiron center was introduced into DFP1. The resulting protein acts as an allosterically modulated phenol oxidase. Its Michaelis–Menten parameters were strongly affected by the binding of ZnP, resulting in a fourfold tighter K m and a 7-fold decrease in k cat . These studies establish the feasibility of designing allosterically regulated catalytic proteins, entirely from scratch. 

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