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Ojewole, A.

Publications and source records attributed to Ojewole, A..

3 recordsLinked to original sources

OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (i) tackle new tasks, like protein-ligand complex structure prediction, (ii) investigate the process by which the model learns, which remains poorly understood, and (iii) assess the models generalization capacity to unseen regions of fold space. Here we report OpenFold, a fast, memory-efficient, and trainable implementation of AlphaFold2. We train OpenFold from scratch, fully matching the accuracy of AlphaFold2. Having established parity, we assess OpenFolds capacity to generalize across fold space by retraining it using carefully designed datasets. We find that OpenFold is remarkably robust at generalizing despite extreme reductions in training set size and diversity, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced by OpenFold during training, we also gain surprising insights into the manner in which the model learns to fold proteins, discovering that spatial dimensions are learned sequentially. Taken together, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial new resource for the protein modeling community.

bioinformatics↗

Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface

The K* algorithm provably approximates partition functions for a set of states (e.g., protein, ligand, and protein-ligand complex) to a user-specified accuracy{varepsilon} . Often, reaching an{varepsilon} -approximation for a particular set of partition functions takes a prohibitive amount of time and space. To alleviate some of this cost, we introduce two algorithms into the osprey suite for protein design: O_SCPLOWFRIESC_SCPLOW, a Fast Removal of Inadequately Energied Sequences, and EWAK*, an Energy Window Approximation to K*. In combination, these algorithms provably retain calculational accuracy while limiting the input sequence space and the conformations included in each partition function calculation to only the most energetically favorable. This combined approach leads to significant speed-ups compared to the previous state-of-the-art multi-sequence algorithm, BBK*. As a proof of concept, we used these new algorithms to redesign the protein-protein interface (PPI) of the c-Raf-RBD:KRas complex. The Ras-binding domain of the protein kinase c-Raf (c-Raf-RBD) is the tightest known binder of KRas, a historically \"undruggable\" protein implicated in difficult-to-treat cancers including pancreatic ductal adenocarcinoma (PDAC). O_SCPLOWFRIESC_SCPLOW/EWAK* accurately retrospectively predicted the effect of 38 out of 41 different sets of mutations in the PPI of the c-Raf-RBD:KRas complex. Notably, these mutations include mutations whose effect had previously been incorrectly predicted using other computational methods. Next, we used O_SCPLOWFRIESC_SCPLOW/EWAK* for prospective design and discovered a novel point mutation that improves binding of c-Raf-RBD to KRas in its active, GTP-bound state (KRasGTP). We combined this new mutation with two previously reported mutations (which were also highly-ranked by O_SCPLOWOSPREYC_SCPLOW) to create a new variant of c-Raf-RBD, c-Raf-RBD(RKY). O_SCPLOWFRIESC_SCPLOW/EWAK* in O_SCPLOWOSPREYC_SCPLOW computationally predicted that this new variant would bind even more tightly than the previous best-binding variant, c-Raf-RBD(RK). We measured the binding affinity of c-Raf-RBD(RKY) using a bio-layer interferometry (BLI) assay and found that this new variant exhibits single-digit nanomolar affinity for KRasGTP, confirming the computational predictions made with O_SCPLOWFRIESC_SCPLOW/EWAK*. This study steps through the advancement and development of computational protein design by presenting theory, new algorithms, accurate retrospective designs, new prospective designs, and biochemical validation.\n\nAuthor summaryComputational structure-based protein design is an innovative tool for redesigning proteins to introduce a particular or novel function. One such possible function is improving the binding of one protein to another, which can increase our understanding of biomedically important protein systems toward the improvement or development of novel therapeutics. Herein we introduce two novel, provable algorithms, O_SCPLOWFRIESC_SCPLOW and EWAK*, for more efficient computational structure-based protein design as well as their application to the redesign of the c-Raf-RBD:KRas protein-protein interface. These new algorithms speed up computational structure-based protein design while maintaining accurate calculations, allowing for larger, previously infeasible protein designs. Using O_SCPLOWFRIESC_SCPLOW and EWAK* within the O_SCPLOWOSPREYC_SCPLOW suite, we designed the tightest known binder of KRas, an \"undruggable\" cancer target. This new variant of a KRas-binding domain, c-Raf-RBD, should serve as an important tool to probe the protein-protein interface between KRas and its effectors as work continues toward an effective therapeutic targeting KRas.

biochemistry↗

OSPREY 3.0: Open-Source Protein Redesign for You, with Powerful New Features

We present O_SCPCAPOSPREYC_SCPCAP 3.0, a new and greatly improved release of the O_SCPLOWOSPREYC_SCPLOW protein design software. O_SCPLOWOSPREYC_SCPLOW 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of O_SCPLOWOSPREYC_SCPLOW when running the same algorithms on the same hardware. Moreover, O_SCPLOWOSPREYC_SCPLOW 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of O_SCPLOWOSPREYC_SCPLOWO_SCPCAP, C_SCPCAPO_SCPLOWOSPREYC_SCPLOW 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that O_SCPLOWOSPREYC_SCPLOW 3.0 accurately predicts the effect of mutations on protein-protein binding. O_SCPLOWOSPREYC_SCPLOW 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open-source software.\n\nO_TEXTBOX\n\nWe present the third major release of the OSPREY protein design software, along with comparisons to experimental data that confirm its ability to optimize protein mutants for desired functions. osprey 3.0 has significant efficiency, ease-of-use, and algorithmic improvements over previous versions, including GPU acceleration and a new Python interface.\n\nC_TEXTBOX\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC=\"FIGDIR/small/306324v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (54K):\norg.highwire.dtl.DTLVardef@f08b0dorg.highwire.dtl.DTLVardef@393b61org.highwire.dtl.DTLVardef@16997ddorg.highwire.dtl.DTLVardef@1713405_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗