DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 4, 12, 15-21, 33, 35 and 40-41 are cancelled by Applicant.
Claims 1-3, 5-11, 13-14, 22-32, 34 and 36-39 are currently pending and are herein under examination.
Claims 1-3, 5-11, 13-14, 22-32, 34 and 36-39 are rejected.
Claims 5, 14, 23-24, 26, 28-32, 34 and 36 are objected.
Priority
The instant application claims domestic benefit as a 371 to International Application PCT/US2020/061526 filed November 20, 2020, which claims domestic benefit to U.S. Provisional Patent Application No. 62/938,021 filed November 20, 2019. The claims to domestic benefit are acknowledged for claims 1-3, 5-11, 13-14, 22-32, 34 and 36-39. As such, the effective filing date for claims 1-3, 5-11, 13-14, 22-32, 34 and 36-39 is November 13, 2019.
Information Disclosure Statement
The IDSs filed 05/19/2022 and 06/08/2022 follow the provisions of 37 CFR 1.97 and have been considered in full. A signed copy of the list of references cited from these IDSs is included with this Office Action.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 204 in para. [84] and 92 in para. [108-109].
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 100 in Figure 1; 400 in Figure 4; and 92A and 92B in Figure 9.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Abstract
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it uses the implied phrases “The disclosure provides”. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Nucleotide and/or Amino Acid Sequence Disclosures
REQUIREMENTS FOR PATENT APPLICATIONS CONTAINING NUCLEOTIDE AND/OR AMINO ACID SEQUENCE DISCLOSURES
Items 1) and 2) provide general guidance related to requirements for sequence disclosures.
37 CFR 1.821(c) requires that patent applications which contain disclosures of nucleotide and/or amino acid sequences that fall within the definitions of 37 CFR 1.821(a) must contain a "Sequence Listing," as a separate part of the disclosure, which presents the nucleotide and/or amino acid sequences and associated information using the symbols and format in accordance with the requirements of 37 CFR 1.821 - 1.825. This "Sequence Listing" part of the disclosure may be submitted:
In accordance with 37 CFR 1.821(c)(1) via the USPTO patent electronic filing system (see Section I.1 of the Legal Framework for Patent Electronic System (https://www.uspto.gov/PatentLegalFramework), hereinafter "Legal Framework") as an ASCII text file, together with an incorporation-by-reference of the material in the ASCII text file in a separate paragraph of the specification as required by 37 CFR 1.823(b)(1) identifying:
the name of the ASCII text file;
ii) the date of creation; and
iii) the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(1) on read-only optical disc(s) as permitted by 37 CFR 1.52(e)(1)(ii), labeled according to 37 CFR 1.52(e)(5), with an incorporation-by-reference of the material in the ASCII text file according to 37 CFR 1.52(e)(8) and 37 CFR 1.823(b)(1) in a separate paragraph of the specification identifying:
the name of the ASCII text file;
the date of creation; and
the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(2) via the USPTO patent electronic filing system as a PDF file (not recommended); or
In accordance with 37 CFR 1.821(c)(3) on physical sheets of paper (not recommended).
When a “Sequence Listing” has been submitted as a PDF file as in 1(c) above (37 CFR 1.821(c)(2)) or on physical sheets of paper as in 1(d) above (37 CFR 1.821(c)(3)), 37 CFR 1.821(e)(1) requires a computer readable form (CRF) of the “Sequence Listing” in accordance with the requirements of 37 CFR 1.824.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed via the USPTO patent electronic filing system as a PDF, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the PDF copy and the CRF copy (the ASCII text file copy) are identical.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed on paper or read-only optical disc, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the paper or read-only optical disc copy and the CRF are identical.
Specific deficiencies and the required response to this Office Action are as follows:
Specific deficiency - The Incorporation by Reference paragraph required by 37 CFR 1.821(c)(1) is missing or incomplete. Incorporation by Reference para. [2] discloses the text file size in KB, but it should be in bytes. See item 1) a) iii) above.
Required response – Applicant must provide:
A substitute specification in compliance with 37 CFR 1.52, 1.121(b)(3) and 1.125 inserting the required incorporation-by-reference paragraph, consisting of:
A copy of the previously-submitted specification, with deletions shown with strikethrough or brackets and insertions shown with underlining (marked-up version);
A copy of the amended specification without markings (clean version); and
A statement that the substitute specification contains no new matter.
Claim Objections
Claims 5, 14, 23-24, 26, 28-32, 34 and 36 are objected to because of the following informalities:
Claim 5 requires a period after the equation.
Claim 14, line 5, recites the word “probably” which should be “probable”.
Claim 23 recites the initialism “RMSD” which should be spelled out first.
Claim 26, last line, recites “contract” which should be “contact”.
Claim 26, lines 4 and 5, should have the article “the” at the beginning of each line to recite “the distance” and “the contact”.
Claims 23-24, 26, 28-32, 34 and 36 recite “Claim” in the preamble which should be “claim” to maintain consistency.
Appropriate correction is required.
Claim Interpretation
35 USC 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are described below:
In claims 1 and 37-39 the structural sampler performs the following function: “sampling at least one configuration of the subject protein according to the topology dataset.”
In claims 1 and 37-39 the structural sampler performs the following function: “calculating a scoring function incorporating a distance between a configuration and the set of probable tertiary motifs.”
In claims 1 and 37-39 the structural sampler performs the following function: “generating a predicted structure representing a local minimum according to the scoring function.”
In claim 3 the structural sampler performs the following function: “sampling … includes sampling the at least one configuration of the subject protein dynamically using at least one of Langevin dynamics and Monte Carlo sampling.”
In claim 6, the structural sampler performs the functions of claim 1.
In claim 14 the structural sampler performs the following function: “sampling the library of tertiary motifs according to their frequency in a reference database to identify probable tertiary motifs.”
In claim 14 the structural sampler performs the following function: “sampling the library of tertiary motifs exhaustively to identify probably tertiary motifs.”
In claim 38 recites “a means for displaying the predicted structure.”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
It appears that the “structural sampler” is a computer-implemented means-plus-function limitation, as evidenced by specification para. [108]. The structure for computer-implemented means-plus-function limitations is the algorithm that performs the function (MPEP § 2181.II.B). Below is a summary of the algorithms in the specification that perform the claimed functions of the “structural sampler”:
In claims 1 and 37-39: “sampling at least one configuration of the subject protein according to the topology dataset”. Specification para. [36-37] recite that sampling can be performed by Langevin dynamics or Monte Carlo sampling. As such, Langevin dynamics or Monte Carlo sampling, and equivalents thereof, will be the structure that performs this function in claims 1 and 37-39.
In claims 1 and 37-39: “calculating a scoring function incorporating a distance between a configuration and the set of probable tertiary motifs”. Specification para. [35] and [89] describe scoring functions that take into account distance between tertiary motifs and a subject protein configuration. As such, these scoring functions will be the structure that performs this function in claims 1 and 37-39.
In claims 1 and 37-39: “generating a predicted structure representing a local minimum according to the scoring function”. Specification para. [73] [91] [104] discuss using the best scoring or lowest score conformation as the final predicted structure. However, there are no algorithms that describe how the structural sampler generates the predicted structure. As such, any algorithm that generates a predicted protein structure based on a local minimum of scoring function will read on this limitation.
In claim 3, the structure for the structural sampler performing “sampling” is the algorithm disclosed in claim 3 which is “sampling at least one configuration of the subject protein dynamically using at least one of Langevin dynamics and Monte Carlo sampling.” The hardware for performing the algorithm is a generic computer as recited in specification para. [19] and in Figure 8.
In claim 6, the structure of the structural sampler is the algorithms disclosed above in the specification for claim 1. The hardware for performing the function of the structural sampler is a generic computer as recited in specification para. [19] and in Figure 8.
In claim 14: “sampling the library of tertiary motifs according to their frequency in a reference database to identify probable tertiary motifs”. Specification para. [48] recites “the tertiary motifs in the library are sampled according to their frequency in a reference database, such as PDB, that is, sampling first from the most frequently-occurring tertiary motifs to the least frequent tertiary motifs.” As such, this algorithm will be the structure.
In claim 14: “sampling the library of tertiary motifs exhaustively to identify probably tertiary motifs”. Neither the specification nor the figures adequately describe how the structural sampler exhaustively samples the library of tertiary motifs. As such, any algorithm that exhaustively samples a library of tertiary motifs will read on this limitation.
The structure in claim 38 for the “means for displaying the predicted structure” is being interpreted as a generic computer display, as described in para. [108] and as depicted in Figures 8 and 9.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
35 USC 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3, 5-11, 13-14, 22-32, 34 and 36-39 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 37-39 fail to comply with the written description requirement because they do not adequately link or associate adequately described particular structure, material, or acts to perform the function recited in the claims identified to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. As discussed in Claim Interpretation, claim 1 recites “generating, by the structural sampler, a predicted structure representing a local minimum according to the scoring function”, which has been interpreted to invoke 35 U.S.C. 112(f). Neither the specification nor the drawings disclose an algorithm that performs this recited function of the structural sampler. Therefore, in accordance with MPEP § 2181.IV, the instant specification does not provide written description support for the structural sampler to perform generating a predicted structure because there is no corresponding algorithm disclosed in the specification.
Furthermore, claims 2-3, 5-11, 13-14, 22-32, 34 and 36 are also rejected because they depend on claim 1, which is rejected, and because they also fail to adequately describe particular structure for the structural sample to perform the above recited function in claim 1.
Claim 14 fails to comply with the written description requirement because it does not adequately link or associate adequately described particular structure, material, or acts to perform the function recited in the claims identified to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. As discussed in Claim Interpretation, claim 14 recites “wherein sampling includes at least one of: … sampling the library exhaustively to identify probably tertiary motifs”, which has been interpreted to invoke 35 U.S.C. 112(f) because the “structural sampler” in claim 1 performs “sampling”. Neither the specification nor the drawings disclose an algorithm that performs the function of the structural sampler recited in claim 14. Therefore, in accordance with MPEP § 2181.IV, the instant specification does not provide written description support for the structural sampler because there is no corresponding algorithm disclosed in the specification.
35 USC 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3, 5-11, 13-14, 22-32, 34 and 36-39 are ejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, line 9, recites the phrase “the set of probable tertiary motifs” which renders the claim indefinite. It is unclear if this phrase refers to both “a set of probable self-tertiary motifs” and “a set of probable pair tertiary motifs”, or if the phrase refers to one of the two sets in particular. To overcome this rejection, clarify what the phrase refers to.
Furthermore, claims 1-3, 5-11, 13-14, 22-32, 34 and 36-39 are also rejected because they depend on claim 1, which is rejected, and because they do not resolve the issue of indefinite.
Claim 5 is indefinite because the variables in the equation have not been defined. To overcome this rejection, define the variables.
Furthermore, claim 7 is rejected because it depends on claim 5, which is rejected, and because it does not resolve the issue of indefinite.
Claim 11, line 5, recites the phrase “the self-tertiary motif” which renders the claim indefinite. It is unclear which self-tertiary motif is being referenced because there more than one self-tertiary motif, as recited in claim 11, lines 3-4. To overcome this rejection, clarify which self-tertiary motif is being referenced.
Claim 11, line 6, recites the phrase “the n-mer” which lacks antecedent basis. To overcome this rejection, provide antecedent basis for the phrase.
Claim 11, lines 6-8, recites twice the phrase “the tertiary motif” which renders the claim indefinite. It is unclear if the phrase refers to a specific self-tertiary motif from which a sequence model was derived as recited in claim 11, line 5, or if the phrase refers to some other tertiary motif. To overcome this rejection, clarify which motif is being referenced.
Claim 11, line 9, recites the phrase “the score” which renders the claim indefinite because it is unclear which of the two scores is being referenced, as recited in claim 11, lines 6-8. To overcome this rejection, clarify which score is being referenced.
Furthermore, claim 13 is also rejected because it depends on claim 11, which is rejected, and because it does not resolve the issue of indefinite.
Claim 14, line 5, recites the relative term “exhaustively”, which renders the claim indefinite. The term “exhaustively” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The metes and bounds of “exhaustively” is unclear because it is a subjective term.
Claim 22 recites the phrase “the self-tertiary motifs in the library” which lacks antecedent basis. Claim 1, lines 2-3, recites “a set of probable self-tertiary motifs from a library” but there is no recitation of just “self-tertiary motif”. To overcome this rejection, provide antecedent basis for the phrase, or clarify that the phrase should read “the probable self-tertiary motifs in the library”.
Furthermore, claims 23-24 are also rejected because they depend on claim 22, which is rejected, and because they do not resolve the issue of indefinite.
Claim 25, line 2, recites the phrase “the pair tertiary motifs in the library”, which lacks antecedent basis. Claim 1, lines 3-4, recites “a set of probable pair tertiary motifs from the library” but there is no recitation of just “pair tertiary motif”. To overcome this rejection, provide antecedent basis for the phrase, or clarify that the phrase should read “the probable pair tertiary motifs in the library”..
Furthermore, claim 26 is also rejected because it depends on claim 25, which is rejected, and because it does not resolve the issue of indefinite.
Claim 27, line 2, recites the phrase “the pair tertiary motifs in the library”, which lacks antecedent basis. Claim 1, lines 3-4, recites “a set of probable pair tertiary motifs from the library” but there is no recitation of just “the pair tertiary motif”. To overcome this rejection, provide antecedent basis for the phrase.
Furthermore, claims 28-29 are also rejected because they depend on claim 27, which is rejected, and because they do not resolve the issue of indefinite.
Claim 30 recites the phrase “the component segments” which lacks antecedent basis. To overcome this rejection, provide antecedent basis.
Claim 31 recites the phrase “the component segments” which lacks antecedent basis. To overcome this rejection, provide antecedent basis.
Claim 32, line 2, recites the phrase “the sequence model of the tertiary motifs” which lacks antecedent basis. To overcome this rejection, provide antecedent basis.
Claim 38 recites the phrase “the non-transient computer-readable medium of claim 36” which lacks antecedent basis because claim 36 does not recite a CRM. For examination purposes, this phrase will be interpreted as “the non-transient computer-readable medium of claim 37”.
Claim 38 recites the phrase “the instructions” which lacks antecedent basis. For examination purposes, claim 38 will be interpreted to depend on claim 37 rather than claim 36.
Claim 39 recites the phrase “the system of claim 31” which lacks antecedent basis because claim 31 does not recite a system. For examination purposes, this phrase will be interpreted as “the system of claim 38”.
The following claims recite the following limitations that invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: in claims 1 and 37-39 “generating, by the structural sampler, a predicted structure representing a local minimum according to the scoring function”, and in claim 14 “wherein sampling includes at least one of: … sampling the library exhaustively to identify probably tertiary motifs”. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and fails to clearly link the structure, material, or acts to the function, as discussed in Claim Interpretation. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-11, 13-14, 22-32, 34 and 36-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a natural phenomenon without significantly more.
Step 2A, Prong 1:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomena (Step 2A, Prong 1). In the instant application, claims 1-3, 5-11, 13-14, 22-32, 34 and 36 recite a method, claim 37 recites a CRM, claim 38 recites a system, and claim 39 recites a method. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claims 1 and 37-39 recite “initializing a structural sampler with a topology dataset comprising: a) a set of probable self-tertiary motifs from a library of tertiary motifs for a subject protein and b) a set of probable pair tertiary motifs from the library of tertiary motifs for the subject protein; sampling, by the structural sampler, at least one configuration of the subject protein according to the topology dataset; calculating, by the structural sampler, a scoring function incorporating a distance between a configuration and the set of probable tertiary motifs; and generating, by the structural sampler, a predicted structure representing a local minimum according to the scoring function.”
Claim 2 recites “calculating the distance as the root-mean squared deviation between a tertiary motif from the library and a fragment from the at least one configuration sampled corresponding to a structure of the tertiary motif.”
Claim 3 recites “wherein sampling, by the structural sampler, includes sampling the at least one configuration of the subject protein dynamically using at least one of Langevin dynamics and Monte Carlo sampling.”
Claim 5 recites “wherein the scoring function is:
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Claim 6 recites “continuously optimizing chain coordinates of the structural sampler to minimize the scoring function.”
Claim 7 recites “wherein the scoring function is minimized by at least one of steepest descent minimization or conjugate gradients minimization.”
Claim 8 recites “wherein the scoring function, in addition to the topology dataset, utilizes one or more molecular mechanical features.”
Claim 9 recites “wherein the one or more molecular mechanical features include one or more of: bond, angle, and dihedral energies; van der Waals and Coulombic interaction energies; and solvation energies.”
Claim 10 recites “wherein the topology dataset further comprises a set of at least one of triplet tertiary motifs, quadruple tertiary motifs, pentuple tertiary motifs, and probable higher-order tertiary motifs.”
Claim 11 recites “determining the set of probable self-tertiary motifs by evaluating the self-tertiary motifs in the library by comparing each contiguous segment along a length of the subject protein according to a sequence model of the self-tertiary motif, calculating a score that indicates a probability of the n-mer conforming to the tertiary motif, or providing a score that indicates a probability of the segments conforming to the tertiary motif, and identifying the set of probable self-tertiary motifs as those for which the score meets or exceeds a reference value.”
Claim 13 recites “wherein the reference value is a pre-determined numerical threshold or pre-determined rank-order.”
Claim 14 recites “wherein sampling includes at least one of: sampling the library of tertiary motifs according to their frequency in a reference database to identify probable tertiary motifs; and sampling the library of tertiary motifs exhaustively to identify probably tertiary motifs.”
Claim 22 recites “wherein the self-tertiary motifs in the library have a length n, and further comprising: generating the self-tertiary motifs by clustering all contiguous n-mers in the library.”
Claim 23 recites “wherein clustering all contiguous n-mers in the library is performed by at least one of best-fit RMSD of backbone atoms or Euclidian distance map norm difference.”
Claim 24 recites “wherein Euclidian distance map norm difference is performed by at least one of greedy clustering, k-means clustering, or hierarchical clustering.”
Claim 25 recites “wherein the pair tertiary motifs in the library have a length n and further comprising: generating the pair tertiary motifs by identifying interacting residue pairs having a distance between alpha carbon atoms and generating a pair of n-mer tertiary motifs having at least one of interacting residue pairs, distance between residue centroids, contact degree-based definition, and other residue orientation-depending geometric descriptors.”
Claim 26 recites “wherein at least one of: interacting residue pairs have a distance between alpha carbon atoms of less than 26 angstroms; distance between residue centroids is less than 25 angstroms; and contract degree-based definition is a contact degree less than 0.8.”
Claim 27 recites “wherein the pair tertiary motifs in the library have a length n, and further comprising: generating the pair tertiary motifs by clustering all pairs of n-mer tertiary motifs in the library.”
Claim 28 recites “wherein clustering all pairs of n-mer tertiary motifs in the library is performed by at least one of best-fit RMSD of backbone atoms and Euclidian distance map norm difference”
Claim 29 recites “wherein the Euclidian distance map norm difference is performed by at least one of greedy clustering, k-means clustering, and hierarchical clustering.”
Claim 30 recites “wherein the component segments of pair tertiary motifs are both the same length.”
Claim 31 recites “wherein the component segments of pair tertiary motifs are different lengths.”
Claim 32 recites “generating the sequence model of the tertiary motifs by employing at least one of a Potts model of tertiary motifs in a cluster and a weak coupling framework of tertiary motifs in a cluster.”
Claim 34 recites “wherein the subject protein is at least one of a de novo protein without a known homologue and less than 3000 amino acids in length.”
Claim 36 recites “wherein the predicted structure exhibits a backbone RMSD less than 3.5_Angstroms, relative to an experimentally-derived structure.”
Claim 39 recites “A method of predicting the structure of a subject protein comprising providing … with a primary amino acid sequence of the subject protein and obtaining the predicted structure.”
Limitations reciting a mental process.
Claims 1, 5, 8-11, 13-14, 22, 25-27, 30-31, 34 and 36-39 contain limitations recited at such a high level of generality that they equate to a mental process because they are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), which the courts have identified as concepts that can be practically performed in the human mind. The paragraphs below discuss the limitations in these claims that recite a mental process under their broadest reasonable interpretation (BRI).
Regarding claims 1 and 37-39, the BRI of initializing a structural sampler includes parameterizing a model, wherein a human can input values into a model. The BRI of sampling includes selecting a configuration at random. The BRI of calculating a scoring function includes performing the calculation of claim 5, which can be done on pen and paper. The BRI of generating a predicted structure representing a local minimum according to the scoring function includes a human evaluating the results of the scoring function to then piece together tertiary fragments that have a local minimum in the subject protein, which can be done using pen and paper.
Regarding claim 5, a human can calculate the scoring function using pen and paper.
Regarding claims 8-10, these limitations are included in the abstract idea in claim 1 because they further limit the data being used to sample the subject protein and calculate the scoring function, but do not change the fact that they are part of the abstract idea.
Regarding claim 11, the BRI of comparing each contiguous segment along a length of the subject protein using a sequence model includes performing a sequence alignment, which a human can do using pen and paper. The BRI of calculating a score that indicates a probability includes a human performing calculations using pen and paper. A human can identify whether a score exceeds a value through analysis and mental determinations.
Regarding claim 13, this limitation is included in the abstract idea in claim 11 of identifying which score meets a reference value because it further limits the reference value but is still an abstract idea.
Regarding claim 14, the BRI of sampling includes a human selecting motifs randomly.
Regarding claims 22 and 27, the motifs having a length n is included in the abstract idea of claim 1 because it further limits the motifs but they are still part of the abstract idea.
Regarding claim 25, the motifs having a length n is included in the abstract idea of claim 1 because it further limits the motifs but they are still part of the abstract idea. The BRI of identifying interacting residue pairs having a distance between alpha carbon atoms includes analyzing data and making a determination. The BRI of generating a pair includes determining that a pair has interacting residues through analysis of data, then piecing them together on pen and paper.
Regarding claim 26, these limitations are included in the abstract idea of claim 25 because they further limit the parameters that a human analyses to generate the motif pairs.
Regarding claims 30-31 and 34, these limitations are included in the abstract idea of claim 1 because they further limit the motifs and subject protein, which are still part of the abstract idea.
Regarding claim 36, this limitation is included in the abstract idea of claim 1 for predicting a structure because it further limits the predicted structure but does not change the fact that it is part of the abstract idea.
Limitations reciting a mathematical concept.
Claims 1, 2-3, 6-7, 11, 22-24, 27-29, 32 and 37-39 recite limitations that equate to a mathematical concept because they are similar to organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)), which the courts have identified as mathematical concepts. The paragraphs below discuss the limitations in these claims that recite a mathematical concept under BRI.
Regarding claims 1 and 37-39, the BRI of a structural sampler includes it being a Metropolis Monte Carlo (MMC), which performs calculations. The BRI of initializing a structural sampler includes parameterizing a MCC, which includes organizing/manipulating numerical variables. The BRI of sampling a configuration includes performing Monte Carlo sampling. The BRI of calculating a scoring function includes using the equation of claim 5.
Regarding claims 2-3 and 5, RMSD, Monte Carlo sampling, and the scoring function are equations.
Regarding claim 6, the specification recites in para. [91] that steepest descent minimization can be used.
Regarding claim 7, steepest descent and conjugate gradient minimization are functions.
Regarding claim 11, calculating a score that indicates a probability is a mathematical concept.
Regarding claims 22-24 and 27-29, the BRI of clustering includes using k-means clustering which performs calculations.
Regarding claim 32, the BRI of a Potts model and a weak coupling framework includes use of mathematical equations and calculations.
Limitations reciting a natural phenomenon.
Claim 1 recites limitations that equate to a natural phenomenon because they are similar to the concept of a correlation between variations in non-coding regions of DNA and allele presence in coding regions of DNA, Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1375, 118 USPQ2d 1541, 1545 (Fed. Cir. 2016), which the courts have established as a natural phenomenon. Claim 1 predicts the structure of a protein from its primary amino acid sequence by using tertiary structures related to segments of the sequence, under its broadest reasonable interpretation. The relationship between primary amino acid sequence and tertiary structure is a natural phenomenon.
As such, claims 1-3, 5-11, 13-14, 22-32, 34 and 36-39 recite an abstract idea and a natural phenomenon (Step 2A, Prong 1: Yes).
Step 2A, Prong 2:
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)). The instant claims recite the following additional elements:
Claim 37 recites “A non-transient computer-readable medium comprising instructions that, upon execution by a microprocessor, causes the microprocessor to perform the method of claim 1.”
Claim 38 recites “A system comprising the non-transient computer-readable medium of claim 36 and a processor for executing the instructions, optionally wherein the system comprises one or more of a human end-user interface and a means for displaying the predicted structure.”
Claim 39 recites “the system of claim 31.”
Regarding the above cited limitation in claims 37-39 of a computer-readable medium (CRM) comprising instructed executed by a microprocessor and a system comprising a CRM and a processor, there are no limitations requiring anything other than a generic computer and/or generic computing system. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
As such, claims 1-3, 5-11, 13-14, 22-32, 34 and 36-39 are directed to an abstract idea and a natural phenomenon (Step 2A, Prong 2: No).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The instant claims recite the following additional elements:
Claim 37 recites “A non-transient computer-readable medium comprising instructions that, upon execution by a microprocessor, causes the microprocessor to perform the method of claim 1.”
Claim 38 recites “A system comprising the non-transient computer-readable medium of claim 36 and a processor for executing the instructions, optionally wherein the system comprises one or more of a human end-user interface and a means for displaying the predicted structure.”
Claim 39 recites “the system of claim 31.”
Regarding the above cited limitation in claims 37-39 of a computer-readable medium (CRM) comprising instructed executed by a microprocessor and a system comprising a CRM and a processor, there are no limitations requiring anything other than a generic computer and/or generic computing system. Therefore these limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they all equate to WURC components of a generic computer and/or generic computing system. Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (Step 2B: No).
As such, claims 1-3, 5-11, 13-14, 22-32, 34 and 36-39 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-6, 8-11, 13-14, 22-32, 34 and 36-39 are rejected under 35 U.S.C. 103 as being unpatentable over Mackenzie et al. (“Mackenzie”; NPL ref. 2 on IDS filed 05/19/2022; Proceedings of the National Academy of Sciences 113, no. 47 (2016): E7438-E7447), as evidenced by Supporting Information of Mackenzie (Proceedings of the National Academy of Sciences, 113(47), E7438-E7447; hereinafter “Mackenzie Supplemental”), in view of Kuhlman et al. (“Kuhlman”; Nature reviews molecular cell biology 20, no. 11 (August, 2019): 681-697), as evidenced by PDB (“2KL8”; published 2009).
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
Claims 1 and 37-39:
A method of predicting the structure of a subject protein comprising:
Mackenzie decomposes known protein structures into basic elements called tertiary structural motifs (TERMs), which are used for protein structure design and prediction (abstract).
initializing a structural sampler with a topology dataset comprising: a) a set of probable self-tertiary motifs from a library of tertiary motifs for a subject protein and b) a set of probable pair tertiary motifs from the library of tertiary motifs for the subject protein;
Mackenzie discloses a dataset DB80 that contains topologies of TERMs (topology dataset) (pg. 7445, col. 2, para. 2) (pg. 7446, col. 2, para. 1). DB80 contains TERMs which can be single-segment motifs (self-tertiary motifs) or multi-segment motifs (pair tertiary motifs) derived from PDB (a library of tertiary motifs) (pg. 7440, col. 1, para. 3) (Figure 6).
The broadest reasonable interpretation of a structural sampler includes a model. Mackenzie Supplemental teaches using the TERMs in a model to determine sequence structure (pg. 3, col. 1, para. 2).
sampling, by the structural sampler, at least one configuration of the subject protein according to the topology dataset;
Mackenzie teaches using “the weak coupling framework reported by Weigt and coworkers, we built a two-body statistical sequence model for each TERM from the MSA of its PDB instances (SI Appendix, SI Methods). With this model, we scored all possible alignments for each of the top 4,000 highest-priority universal TERMs (with up to three segments) onto each protein sequence from the above X-ray-1 and NMR-1 sets” (pg. 7443, col. 2, para. 4).
However, Mackenzie does not teach sampling a configuration of a subject protein.
Kuhlman reviews advances in protein structure and prediction and design (title). Kuhlman teaches Monte Carlo (MC) simulations are used for protein structure prediction (Box 1) (Figure 2). The Monte Carlo simulation starts “from a random or fully extended conformation and proceed by repeatedly selecting a random window of the protein (e.g. residues 22–30) and inserting into that window the structure of a randomly selected fragment from the corresponding fragment library” (pg. 684, col. 2, para. 2). Box 1 shows the local and global minima of the MC simulation.
calculating, by the structural sampler, a scoring function incorporating a distance between a configuration and the set of probable tertiary motifs; and
Mackenzie Supplemental teaches “The top 20 best-scoring alignments were found and recorded for each TERM, with the best alignments from the combined list corresponding to predicted TERM alignments. Alignments were considered structurally correct if the corresponding RMSD was below 1.0 Å for single-segment motifs, below 1.5 Å for two-segment motifs, and below 2.0 Å for three-segment motifs” (pg. 3, col. 1, para. 2).
generating, by the structural sampler, a predicted structure representing a local minimum according to the scoring function.
Mackenzie Supplemental shows in Figure S19 an example of a predicted structure of a protein based on its sequence using TERMs. In Figure S19 single-segment (self-tertiary motifs) and multi-segment (pair tertiary motifs) TERMs are used to construct the structure of the protein from its sequence. Figure 6 shows that the predictions were made for de novo protein sequences as well.
However, Mckenzie does not teach that the predicted structure is based on a local minimum according to the scoring function.
Kulhman teaches “This hypothesis forms the basis for a general approach to protein structure prediction that combines sampling of alternative conformations with scoring to rank them by energy and identify the lowest energy state” (pg. 682, col. 2). Box 1 also shows the MC simulation evaluating local minima.
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Mackenzie for predicting protein structure by using a MC simulation that performs protein configuration sampling to find local energy minima as taught by Kulhman. The motivation for doing so is taught by Kulhman who states that MC simulations lead to a lowest-energy model when using fragment assembly (pg. 684, col. 2, para. 2). One of ordinary skill in the art would have had a reasonable expectation of success because Kulhman teaches that MC simulations are used on fragment assemblies (Box 1) (pg. 684, col. 2, para. 2). Mackenzie teaches using fragments (i.e., TERMs) to predict structure from sequence.
Claim 2:
Mackenzie Supplemental teaches “The top 20 best-scoring alignments were found and recorded for each TERM, with the best alignments from the combined list corresponding to predicted TERM alignments. Alignments were considered structurally correct if the corresponding RMSD was below 1.0 Å for single-segment motifs, below 1.5 Å for two-segment motifs, and below 2.0 Å for three-segment-motifs” (pg. 3, col. 1, para. 2). Each TERM corresponds to a portion of a protein sequence.
Claim 3:
As discussed above regarding claim 1, Mackenzie teaches aligning sequence models of TERMs onto each protein sequence in the X-ray-1 and NMR-1 datasets (pg. 7443, col. 2, para. 4). However, Mackenzie does not teach sampling configurations of a subject protein. Kulhman teaches using MC simulations to sample configurations based on fragments (Box 1) (Figure 2).
Claims 5 and 9:
Mackenzie Supplemental teaches scoring the simulated structures (pg. 3, col. 2, para. 2-3). However, Mackenzie does not disclose the scoring function of instant claim 5.
Kulhman teaches “Typical protein energy functions are linear combinations of multiple terms, each term capturing a distinct energetic contribution (van der Waals interactions, electrostatics)” (caption to left of Box 1).
It would have been prima facie obvious to one of ordinary skill in the art to have modified the scoring function of Mackenzie used to predict protein structure based on amino acid sequence by using the scoring function of Kulhman. The motivation for doing so is taught by Kulhman who recites that their energy functions navigate protein conformational energy landscapes to allow for accurate protein structure prediction (top caption of Box 1). One of ordinary skill in the art would have had a reasonable expectation of success to use the energy functions of Kulhman in Mackenzie because they are applicable to fragment assemblies (Box 1). Mackenzie uses a fragment assembly method (i.e., TERMs).
Claim 6:
Mackenzie teaches “TERM-based backbone decomposition thus appears to strike a balance between interpreting backbone coordinates loosely enough to recognize similar conformations as representing related ensembles, and yet precisely enough to suggest native-like sequences” (pg. 7442, col. 2, para. 2). Mackenzie also teaches using a scoring function to predict protein structure (pg. 7443, col. 2, para. 4). However, Mackenzie does not teach continuously optimizing chain coordinate of their model to minimize the scoring function.
Kulhman teaches “In gradient-based optimization approaches (see the figure, upper left panel), the derivatives of the energy function with respect to the flexible degrees of freedom (e.g. the atomic coordinates or backbone torsion angles) are calculated in order to proceed in the direction in which the energy decreases most rapidly. Gradient-based optimization is effective at finding the nearest local minimum in the energy landscape” (caption of Box 1). Kulhman also discusses model refinement techniques performed after fragment assembly (pg. 685, col. 2 – pg. 686, col. 1, para. 1).
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Mackenzie for predicting protein structure by using a gradient-based optimization approach that optimizes atomic coordinates to determine a local minimum as taught by Kulhman. The motivation for doing so is taught by Kulhman who states that these energy functions help guide protein prediction (caption to the left of Box 1). One of ordinary skill in the art would have had a reasonable expectation of success because Kulhman states that scoring functions are used to predict protein structure (Box 1) (Figure 2).
Claim 8:
Mackenzie Supplemental discloses calculating pseudo-energies (molecular mechanical features) for TERMs (topology dataset) used in the structure predictions, wherein the predicted structures are scored in part based on the pseudo-energies (pg. 3, col. 1, para. 2).
Claim 10:
Mackenzie shows in Figure 6 that 3-segment TERMs were used.
Claim 11:
determining the set of probable self-tertiary motifs by evaluating the self-tertiary motifs in the library by comparing each contiguous segment along a length of the subject protein according to a sequence model of the self-tertiary motif,
Mackenzie teaches “Using the weak coupling framework reported by Weigt and coworkers (70), we built a two-body statistical sequence model for each TERM from the MSA of its PDB instances (SI Appendix, SI Methods). With this model, we scored all possible alignments for each of the top 4,000 highest-priority universal TERMs (with up to three segments) onto each protein sequence from the above X-ray-1 and NMR-1 sets” (pg. 7443, col. 2, para. 4).
calculating a score that indicates a probability of the n-mer conforming to the tertiary motif, or providing a score that indicates a probability of the segments conforming to the tertiary motif, and identifying the set of probable self-tertiary motifs as those for which the score meets or exceeds a reference value.
Mackenzie Supplemental teaches “The final score for each alignment 𝑘 of TERM 𝑡 was then calculated as:
PNG
media_image2.png
90
656
media_image2.png
Greyscale
where 𝐸 (𝑡) is statistical energy associated with the 𝑘-th alignment of TERM 𝑡, calculated by summing the appropriate self and pair energy components (more positive energies are more favorable by convention in Morcos et al. (6)), the sum in the denominator on the left extends over all possible alignments of 𝑡 in the corresponding benchmark protein, and 𝑝 𝑡 represents the prior probability of observing the TERM and was taken simply as the frequency of 𝑡 in the set-cover database. The top 20 best-scoring alignments were found and recorded for each TERM, with the best alignments from the combined list corresponding to predicted TERM alignments” (pg. 3, col. 2, para. 2).
Claim 13:
Mackenzie Supplemental recites “Alignments were considered structurally correct if the corresponding RMSD was below 1.0 Å for single-segment motifs, below 1.5 Å for two-segment motifs, and below 2.0 Å for three-segment motifs” (pg. 3, col. 2, para. 2).
Claim 14:
Mackenzie teaches using the TERMs to predict the structure of a protein, wherein top-ranking TERMs are selected (pg. 7443, col. 2, para. 4). However, Mackenzie does not teach sampling exhaustively the TERMS.
Kuhlman teaches “Examples of the move sets used for Monte Carlo simulations include fragment-replacement moves, in which a continuous backbone segment in the current conformation is replaced with an alternative conformation from a fragment library, and side-chain rotamer substitutions”, which is done to achieve an energy minimum (caption of Box 1).
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Mackenzie for predicting protein structure by using a MC simulation that performs protein configuration sampling to find local energy minima as taught by Kulhman. The process would be performed “exhaustively” until a satisfactory energy minimum is found, which Kulhman teaches is advantageous (pg. 684, col. 2, para. 2). One of ordinary skill in the art would have had a reasonable expectation of success because Kulhman teaches that MC simulations use fragment assemblies (Box 1) (pg. 684, col. 2, para. 2). Mackenzie teaches using fragments (i.e., TERMs) to predict structure from sequence (pg. 7443, col. 2, para. 3-4).
Claims 22-24 and 27-29:
Mackenzie teaches single-segment TERMs (self-tertiary motifs) and multi-segment TERMs (pair tertiary motifs) acquired from PDB (the library) (pg. 7440, col. 1, para. 3), which are protein segments with lengths (length of n) (pg. 7440, col. 1, para. 4). These TERMs make up the DB80 dataset (pg. 7445, col. 2, last para.). The TERMs in the DB80 dataset were greedily clustered (clustering all contiguous n-mers in the library) (Euclidean distance map norm difference) (greedy clustering) (pg. 7446, col. 1, para. 1).
Claim 25:
Mackenzie recites “The universal set to cover consisted of all unique residues and PCs in DB80, representing secondary and tertiary/quaternary information, respectively” (the pair tertiary motifs in the library have a length n) (pg. 7445, col. 2, last para.). Mackenzie Supplemental teaches “Residues consecutive in sequence are joined by peptide bonds of relatively fixed geometry, so that the position of each next residue in a chain is highly constrained by the previous residues (e.g., Cα-to-Cα distances between adjacent residues are generally ~3.8 Å) (interacting residue pairs having a distance between alpha carbon atoms) (pg. 1, col. 1, para. 1). Mackenzie teaches how the motifs were created which includes residue pairs capable of making contact (PCs) which is a measure of contact degree (generating a pair of n-mer tertiary motifs having at least one of contact degree-based definition) (pg. 7445, col. 2, para. 3-4).
Claim 26:
Mackenzie teaches how the motifs were created which includes residue pairs capable of making contact (PCs) which is a measure of contact degree (contact degree-based definition) (pg. 7445, col. 2, para. 3-4). The contact degree varies from 0 to 1 (contact degree less than 0.08). MPEP 2144.05.I recites “In the case where the claimed ranges ‘overlap or lie inside ranges disclosed by the prior art’ a prima facie case of obviousness exists.” Therefore, it would have been prima facie obvious to require a contact degree of less than 0.8 in Mackenzie because the claimed range of less than 0.8 overlaps with the ranged disclosed by Mackenzie of 0 to 1.
Claim 30:
Mackenzie Supplemental shows in Figure S2B a motif with two segments of the same length: n = [5,5].
Claim 31:
Mackenzie Supplemental shows in Figure S2 a motif with two segments, wherein one segment is 5 and the other second can be n-residues, which includes the residues being other than 5. See caption of Figure S2.
Claim 32:
Mackenzie teaches “Using the weak coupling framework reported by Weigt and coworkers (70), we built a two-body statistical sequence model for each TERM from the MSA of its PDB instances (SI Appendix, SI Methods)” (pg. 7443, col. 2, para. 4). Mackenzie also teaches that the TERMs are clustered (pg. 7446, col. 1, para. 1).
Claim 34:
Mackenzie teaches referring to predicting protein structure “TERM-based mining appears to be quite applicable to de novo proteins and requires no homology (Fig. 6)” (pg. 7445, col. 1, para. 2). Figure 6 shows that one of the de novo proteins is 2KL8, which was acquired from PDB. As evidenced by PDB, 2KL8 is 85 amino acids in length.
Claim 36:
Mackenzie Supplemental teaches “Alignments were considered structurally correct if the corresponding RMSD was below 1.0 Å for single-segment motifs, below 1.5 Å for two-segment motifs, and below 2.0 Å for three-segment motifs” (pg. 3, col. 1, para. 2). See also Figure S6.
Claims 37-38:
Mackenzie discloses that their method is computer-implemented by use of several software packages such as MASTER (pg. 7440, col. 1, para. 1), wherein computers have memory and processors.
Claim 39:
Mackenzie discloses a computer-implemented method, wherein the structure of a protein is predicted based on its sequence (pg. 7443, col. 2, para. 3-4). See also Figure S6.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Mackenzie et al. (“Mackenzie”; NPL ref. 2 on IDS filed 05/19/2022; Proceedings of the National Academy of Sciences 113, no. 47 (2016): E7438-E7447), as evidenced by Supporting Information of Mackenzie (Proceedings of the National Academy of Sciences, 113(47), E7438-E7447; hereinafter “Mackenzie Supplemental”), in view of Kuhlman et al. (“Kuhlman”; Nature reviews molecular cell biology 20, no. 11 (August, 2019): 681-697), as evidenced by PDB (“2KL8”; published 2009), as applied above to claims 1 and 5, and in further view of Ramachandran et al. (“Ramachandran”; Proteins: Structure, Function, and Bioinformatics 79, no. 1 (2011): 261-270).
The limitations of claims 1 and 5 have been taught in the rejection above by Mackenzie and Kuhlman.
Claim 7:
Mackenzie discloses scoring functions (pg. 7442, col. 2, para. 2). Kuhlman discloses gradient descent-based minimization and protein energy functions (Box 1). However, Mackenzie and Kulhman do not teach using either steepest descent or conjugate gradients to minimize a scoring function.
Ramachandran automates minimization of steric classes during protein structure prediction (abstract). Ramachandran states “Several tools have emerged for resolution of such clashes upon identification. Steepest descent/conjugate gradient minimization using all-atom molecular mechanics force fields is the most widely used method to resolve clashes in a protein structure before using the structure for further studies” (pg. 261, last para.).
It would have been prima facie obvious to one of ordinary skill in the art to have minimized the energy functions of Mackenzie and Kulhman with steepest descent or conjugate gradient minimization as taught by Ramachandran because Ramachandran states that they resolve clashes in protein structure when predicting protein structure (pg. 261, last para.). One of ordinary skill in the art would have had a reasonable expectation of success for the combination because Ramachandran states that steepest descent and conjugate gradient minimization can be used in an all-atom model (pg. 261, last para.). Kulhman discloses structure prediction models that use atomic representations and protein energy functions such as molecular dynamics (Box 1) (Figure 1).
Conclusion
No claims are allowed.
Notable, but not relied upon, prior art includes: Blattner et al. (WO 2017/011779 A1) protein structure prediction. O’Meara et al. (Journal of chemical theory and computation 11, no. 2 (2015): 609-622) structure prediction. Tang et al. (In Biocomputing 2005, pp. 370-381. 2005) clustering short protein segments having strong sequence structure correlations and contain useful structural information for protein structure prediction. Cheng et al. (IEEE reviews in biomedical engineering 1 (2008): 41-49) machine learning models for protein structure prediction. Zhou et al. (bioRxiv (2018): 431635) protein design based on mining sequences-structure relationships in known protein structures. Wang et al. (BMC bioinformatics 20, no. Suppl 3 (2019): 135) energy functions for protein structure prediction.
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685